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It is essential for meeting the stringent real-time demands encountered in actual production scenarios. Employing the low computational complexity of recursive algorithms, some new schemes are developed for the parameter estimation of a class of time-varying systems. The temporal evolution of parameters is characterized through the autoregressive process, facilitating the construction of the identification model with regard to the autoregressive coefficients. Based on the computational efficiency of the gradient search, a parametric autoregression-based stochastic gradient algorithm is derived with an appropriate step size, achieving a compromise between the steepest descent and convergence rate. In order to address the limitation of the low estimation accuracy in gradient algorithms, a parametric autoregression-based multi-innovation stochastic gradient algorithm is explored by making use of the favorable information for corrections. The simulation results are given to demonstrate the effectiveness of the proposed algorithms. Therefore, for a class of time-varying systems whose parameters become the further insight through the autoregressive process, the proposed gradient methods can obtain the parameter estimates faster and more accurately while ensuring the real-time performance of time-varying systems. [ABSTRACT FROM AUTHOR]

ABSTRACT This article is aimed to study the parameter estimation problems of a non‐commensurate fractional‐order system with saturation and dead‐zone nonlinearity. In order to reduce the structural complexity of the system, the model separation scheme is used to decompose the fractional‐order nonlinear system into two subsystems, one includes the parameters of the linear part and the other includes the parameters of the nonlinear part. Then, we derive an auxiliary model separable gradient‐based iterative algorithm with the help of the model separation scheme. In addition, to improve the utilization of the real time information, an auxiliary model separable multi‐innovation gradient‐based iterative algorithm is presented based on the sliding measurement window. Finally, the feasibility of the presented algorithms is validated by numerical simulations. [ABSTRACT FROM AUTHOR]

In the quest for sustainable and efficient energy solutions, hydrogen fuel cells emerge as a beacon of hope, offering a promising pathway towards a greener future. Accurate Identification of the ungiven parameters of proton exchange membrane fuel cell (PEMFC) mathematical models is indispensable for designing, managing, and simulating the practical PEMFC. In order to identify the parameters of PEMFC punctually, this paper presents a modified version of the slime mould algorithm (MSMA). In order to increase capability of the MSMA in the exploitation phase, both locally and globally, the sine-cosine technique has been utilized to boost the search capabilities. To assess the performance of MSMA, MSMA is first utilized to address ten well-known benchmark functions. The obtained results confirm that MSMA outperforms SMA on all benchmark functions. Then, MSMA is employed to solve the optimization problem of different mechanical design problems and also the MSMA provides superior performance over the standard SMA. Finally, the MSMA is used to identify the unknown parameters of four typical PEMFCs: 250W PEMFC, BCS 500W PEMFC, AVISTA SR-12 model, and the Temasek 1 kW PEMFC model. Experimental results boost the supremacy of MSMA in the PEMFC parameters extraction by comparing it with the original SMA and well-known potent optimization techniques. Furthermore, MATLAB/Simulink is employed for advanced dynamic PEMFC modeling, facilitating a comprehensive assessment of fuel cell parameters. The validation of this dynamic PEMFC model, using MSMA-optimized parameters, establishes its practical utility in system analysis and real-world fuel cell operation, marking a significant advancement in PEMFC technology management and simulation. [ABSTRACT FROM AUTHOR]

Inverse problems are commonly used in many fields as they enable the estimation of parameters that cannot be experimentally measured. However, the complex nature of inverse problems requires a strong background in data and signal processing. Moreover, ill-posed problems yield parameters that have a strong linear dependence on the problem. The ill-posed nature of these problems lead to many errors in numerical computations that can make parameter identification nearly impossible. In this paper, a new data processing tool is proposed to maximize the sensitivity of the model to the parameters of interest, while reducing the correlation between them. The effectiveness of the toll is demonstrated through a given inverse problem example using Periodically Pulsed Photothermal Radiometry (PPTR). [ABSTRACT FROM AUTHOR]

Incorporating PEM electrolyzers (PEMElz) into microgrid systems presents a promising avenue for utilizing surplus renewable energy to produce hydrogen while meeting the system operation requirements. The accurate emulation of the behavior of PEMElz in such systems requires a detailed representation of their electrochemical and thermal characteristics. Yet modeling these characteristics optimally poses a challenge due to their inherent complexity, characterized by multivariate, non-linear, and multi-modal characteristics. To address this challenge, this paper proposes an advanced approach to model PEMElz by introducing general forms for the integrated electrochemical and thermal model based on a set of optimally identified parameters. The introduced general forms are generated using an n-dimensional polynomial fitting process for a set of optimally identified parameters, which are determined using the Partial Reinforcement Optimizer (PRO) under various temperature and pressure levels. These general forms provide a reliable PEMElz model to adapt to any temperature and pressure operating conditions. The proposed approach's effectiveness in capturing the static characteristics of PEMElz is verified by analyzing polarization and thermal curves across multiple operating conditions. Furthermore, the proposed general forms are implemented in Matlab/Simulink to emulate the PEMElz behavior when it is incorporated into a microgrid dominated by high penetration levels of variable generation resources such as solar photovoltaic (PV). This demonstration highlights the direct relevance and tangible impact of the proposed general-formulated model based on the optimal identified parameters approach in various operating scenarios. • Identified optimal parameters for a combined electrochemical and thermal PEMElz. • Tailored partial reinforcement optimizer (PRO) for PEMElz parameter identification. • Deriving general forms for the PEMElz model parameters. • Investigated proposed PEMElz in microgrid system. • The general forms are a computationally effective tool reflecting PEMElz performance. [ABSTRACT FROM AUTHOR]

ABSTRACT This article is aimed to study the parameter identification of the ExpARX system. To overcome the computational complexity associated with a large number of feature parameters, a parameter separation scheme based on the different features of the identification model is introduced. In terms of the phenomenon that the coupling parameters lead to the inability of algorithms, a separable synchronous interactive estimation method is introduced to eliminate the coupling parameters and perform parameter estimation in accordance with the hierarchical principle. For the purpose of achieving high‐accuracy performance and reducing complexity, a separable synchronous gradient iterative algorithm is derived by means of gradient search. In order to improve the identification accuracy, a separable synchronous multi‐innovation gradient iterative algorithm is proposed by introducing the multi‐innovation identification theory. In order to improve the convergence speed, a separable synchronous multi‐innovation conjugate gradient iterative algorithm is proposed by introducing the conjugate gradient theory. Finally, a simulation example and a real‐life example of piezoelectric ceramics are used to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Introduction: Fuel cell technology is a harbinger of the future for generating electricity due to their high efficiency and low emissions achieved through the direct conversion of chemical energy into electrical energy without combustion. Methods: To optimize the design and performance, a fuel cell model is essential to predict its behaviour in different conditions. This technical note presents a novel physics-based approach, the Young's Double-slit Experiment Optimizer (YDEO), for identifying parameters in Proton Exchange Membrane Fuel Cells. A performance metric is established by formulating an objective function that relies on the summation of squared errors between experimental and estimated values. Results and discussion: The effectiveness of this approach is evaluated through the analysis of four benchmark test cases: Horizon 500 W, BCS500 W, NedstackPS6, and 250 W. The corresponding objective function values for these test cases are 0.011243, 2.065557, 0.011698, and 5.250849, respectively. The simulation results demonstrate the efficacy of the proposed YDEO algorithm when compared with other existing popular and contemporary algorithms in the literature. [ABSTRACT FROM AUTHOR]

Summary: In industrial process control systems, parameter estimation is crucial for controller design and model analysis. This article examines the issue of identifying parameters in continuous‐time models. This article presents a stochastic gradient estimation algorithm and a recursive least squares estimation algorithm for identifying the parameters of continuous systems. It derives the parameter identification model of linear continuous‐time systems based on the Laplace transforms of the input and output of the systems. To prove that the techniques given here work, we have included a simulated example. [ABSTRACT FROM AUTHOR]

Considering the electromagnetic interference signal in non-cooperative communication, an automatic modulation identification and detection system of electromagnetic interference signal based on software defined radio is proposed. Based on GNU Radio 3.10.7.0 and HackRF One B210mini, the system estimates the frequency and symbol rate of the interference signal and completes clock synchronization and matching filtering under the condition of unknown a priori information. By extracting high-order cumulants as characteristic parameters, combined with the decision tree classifier, the classification and recognition of six modulation types of interference signals and signal phase correction are realized. This method can distinguish the recognition results in combination with the signal constellation, and complete the real-time reception and recognition of interference signals. [ABSTRACT FROM AUTHOR]

This paper presents a new adaptive modification of active disturbance rejection control (ADRC) with parameter estimation based on a recursive least-squares (RLS) method. The common ADRC used in many applications relies on the simple approach, which assumes the simplification of the object into an integral chain form. However, this model-free ADRC does not guarantee the stability of a closed-loop system in the presence of noticeable modeling uncertainties, so it is compared in this paper to another approach, in which the linear part of the system is included in the ADRC framework (generalized ADRC). This incorporation of the model is examined in the paper for a wide range of model and controller parameters, considering also the presence of external disturbances as well as parameter uncertainties, pointing out the limitations of fixed-gain algorithms. Then, the adaptive modification of the model-based ADRC is proposed, which is equipped with a real-time estimation of model parameters by means of the RLS method in continuous time. The stability conditions of the proposed modification of the algorithm in the closed control loop are also analyzed. It can be concluded that, under appropriate conditions, the inclusion of information about known plant parameters into the ADRC can noticeably improve the conditions of the control system. The proposed adaptive model-based approach enables quality improvement during the control process even with initially unknown parameters, for time-varying parameters, and in the presence of parametric uncertainties and external disturbances. The tests were performed on a real plant—the task of controlling the angular velocity of the direct current (DC) motor was considered. [ABSTRACT FROM AUTHOR]

Solar energy provides one of the most favorable options regarding the transition to clean energy sources. The parameters of a photovoltaic (PV) system play determine its performance under various scenarios. The PV model parameter estimation is an example of nonlinear planning. This study proposes a novel use of the established Smell Agent Optimizer (SAO) algorithm to anticipate the undefined parameters of the PV model's single-diode and two-diode equivalent circuits. This study aims to create a precise PV model that can accurately characterize its performance under changing operational conditions. The desired objective function is defined as the square of the mean squared error between the model's current-voltage curve and the measured curve. [ABSTRACT FROM AUTHOR]

This paper discusses the identification problem of the nonlinear exponential autoregressive model in the non‐Gaussian noise environment. To suppress the negative influence caused by the non‐Gaussian noise on the accuracy of the identification, this paper employs a Cauchy kernel correntropy‐based criterion function to present a robust gradient algorithm for calculating the parameter estimates of the model. To solve the difficulty of selecting the step‐size in the gradient algorithm, this paper derives an optimal step‐size related to the bandwidth of the Cauchy kernel. Moreover, in order to improve the accuracy of the parameter estimations, a Cauchy kernel correntropy‐based robust multi‐innovation identification method is developed by utilizing the multi‐innovation identification theory. Two simulation examples exhibit that the proposed algorithms can effectively reduce the negative influence of the non‐Gaussian noise on parameter estimation in contrast with the conventional least mean squares algorithm. [ABSTRACT FROM AUTHOR]

High-quality identification schemes are essential for controller design of nonlinear systems in the field of automatic control. In control communities, the prescribed performance technology has received wide attention as a promising methodology because of its quantitative description of the steady-state and transient performance of system control over the past several years. The improved transient performance of the parameter estimation is widely known to enable simplified controller design, reduce system regulation time, and prevent system divergence. However, in system identification communities, few studies on the transient performance of parameter identification have been published because of the difficulties in designing an error variable that reflects the transient performance of parameter identification. To address this problem, this study provides a solution by integrating the prescribed performance technique into the estimator design. In this study, we introduced an adaptively prescribed performance parameter identification for nonlinear sandwich systems subjected to quantitative observations. First, a low-pass filter and forcing variables are developed to construct an identification error expression. Subsequently, an improved prescribed performance function that characterises the error bound of parameter estimation is introduced. Second, the error transformation concept is used to obtain a new system by transforming the raw system such that constraint conditions are avoided. A novel adaptive law is proposed to guarantee original parameter identification using the prescribed performance. Finally, simulation and process examples are presented to illustrate finding results. [ABSTRACT FROM AUTHOR]

The growing use of hydrogen as an energy carrier in the context of a renewed energy landscape is showing very promising potential. Most notably, polymer electrolyte membrane fuel cells (PEMFCs) are at the heart of the energy revolution and provide an efficient alternative to fossil fuels. The need for accurate modeling to analyze and develop the performance of PEMFCs has prompted many researchers to make major efforts in this area. This paper proposes a new approach based on an enhanced version of the bio-inspired metaheuristic salp swarm algorithm (ESSA) to identify the unknown parameters of the PEMFC model. This innovative approach was implemented by the combination of the merits of four approaches, namely the opposition-based learning, the new category of salps, referred to as exploration salps, the crossover and mutation operators, and the survival of the fittest mechanism. The newly introduced mechanisms succeed to overcome the problem of low population diversity of the SSA algorithm, accelerate its speed of convergence, strengthen the salp exploration capabilities, and improve its convergence accuracy. The performance of this technique has been tested and validated experimentally on the voltage–current characteristic on two commercial PEMFC stacks, such as 250 W and BCS500 W. The found results are compared to those of the original SSA and six recently published competitive metaheuristic algorithms simulated in various conditions as well as nine other well-cited methods from the literature. The performance comparison involved the calculation of the sum of squared errors (SSE), Standard Deviation (STD), computational speed, and statistical measurements. In this context, the best SSE and STD values obtained for Case I range 2 were 0.60132 and 1.0597 E − 14 , while those of Case II were 0.01167 and 1.1127E−15, respectively. Based on the results, the ESSA-based approach demonstrates superior performance in determining PEMFC parameters from experimental measurements, offering enhanced stability, faster convergence speed, and greater accuracy. This method thus emerges as a highly promising avenue for PEMFC parameter estimation, providing a valuable tool for optimizing fuel cell performance and design. • Modeling and parameter extraction methods for PEMFC models are investigated. • A new ESSA is proposed to tackle the parameterization problem of PEMFC. • ESSA uses OBL, exploration salps, crossover and mutation operators, and survival of the fittest mechanism. • Experimental tests show ESSA's best accuracy, convergence speed, and stability for 250 W and BCS500 W PEMFC. • ESSA is useful to develop precise simulators, and master PEMFC design and control. [ABSTRACT FROM AUTHOR]

This article considers the parameter identification problems of stochastic systems which described by the finite impulse response moving average model. Since the system is disturbed by colored noise, we introduce the data filtering technique from a view point of improving the parameter estimation accuracy. The data filtering technique is to use a filter to filter the input and output data of the system disturbed by colored noise so as to improve the identification accuracy. By using the data filtering technique, this article proposes a filtering‐based recursive extended least squares (F‐RELS) algorithm. The convergence analysis indicates that the parameter estimates can converge to their true values. Compared with the recursive extended least squares algorithm, the proposed F‐RELS algorithm can obtain more accurate parameter estimation. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The Proton Exchange Membrane Fuel Cell (PEMFC) has found widespread use for regulated output voltage applications because of its quick response time and mobility. A different form of hydrogen is employed in fuel cell-based electric vehicles for smart transportation for the reduction of global warming and the development of smart cities. To properly manage the operation of Fuel Cells (FCs), there is a need for accurate modeling. One of the most common challenges is finding the exact values of unknown parameters in the PEMFC. In the current study, a new method called Dandelion Optimizer (DO) is used for parameter identification. DO is used to estimate the parameters of the PEMFC based on Current-Voltage (I-V) characteristics. The Ballard Mark V and BCS 500-W PEMFC stacks use the DO method to identify unknown parameters. The performance of the DO algorithm is compared to that of other optimization techniques and the Sum of Squared Errors (SSE) is used to represent the objective function of the current optimization problem. In contrast to traditional and other efficient techniques, the simulation results proposed by the DO algorithm have excellent accuracy in extracting the PEMFC optimal parameters. [ABSTRACT FROM AUTHOR]

With the advancement of network technology, there has been an increase in the volume of data being transmitted across networks. Due to the bandwidth limitation of communication channels, data often need to be quantized or event-triggered mechanisms are introduced to conserve communication resources. On the other hand, network uncertainty can lead to data loss and destroy data integrity. This paper investigates the identification of finite impulse response (FIR) systems under the framework of stochastic noise and the combined effects of the event-triggered mechanism and uncertain communications. The study provides a reference for the application of remote system identification under transmission-constrained and packet loss scenarios. First, a congruential summation-triggered communication scheme (CSTCS) is introduced to lower the communication rate. Then, parameter estimation algorithms are designed for scenarios with known and unknown packet loss probabilities, respectively, and their strong convergence is proved. Furthermore, an approximate expression for the convergence rate is obtained by data fitting under the condition of uncertain packet loss probability, treating the trade-off between convergence performance and communication resource usage as a constrained optimization problem. Finally, the rationality and correctness of the algorithm are verified by numerical simulations. [ABSTRACT FROM AUTHOR]

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The electrochemical proton exchange membrane fuel cell (PEMFC) is an electrical generator that utilizes a chemical reaction mechanism to produce electricity, serving as a sustainable and environmentally friendly energy source. To thoroughly analyze and develop the features and performance of a PEMFC, it is essential to use a precise model that incorporates exact parameters to effectively suit the polarization curve. In addition, parameter extraction plays a crucial role in the simulation analysis, evaluation, optimum control, and fault detection of the proton exchange membrane fuel cell (PEMFC) system. Despite the development of many algorithms for parameter extraction in PEMFC, obtaining accurate and trustworthy results rapidly remains a challenge. This study presents a hybridized algorithm, namely differential evolution ameliorated (DEA) for reliably estimating PEMFC model parameters. To evaluate the proposed DEA-based parameter identification, a comparison analysis with previously published methods is conducted using MATLAB/SimulinkTM (R2016b, MathWorks, Natick, MA, USA) in terms of system correctness and convergence process. The proposed DEA algorithm is tested to extract the parameters of two PEMFC models: SR-12 500 W and 250 W. The sum of the squared errors (SSE) between the experimental and the obtained voltage data is defined as an objective function. The simulation results prove that the suggested DEA algorithm is capable of identifying the optimal PEMFC parameters rapidly and accurately in comparison with other optimization algorithms. [ABSTRACT FROM AUTHOR]

Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best accounts of the data. Cognitive modeling uses statistical tools to quantitatively relate models to data that often rely on computing/estimating the likelihood of the data under the model. However, this likelihood is computationally intractable for a substantial number of models. These relevant models may embody reasonable theories of cognition, but are often under-explored due to the limited range of tools available to relate them to data. We contribute to filling this gap in a simple way using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. We test our instantiation of an ANN as a cognitive model fitting tool on classes of cognitive models with strong inter-trial dependencies (such as reinforcement learning models), which offer unique challenges to most methods. We show that we can adequately perform both parameter estimation and model identification using our ANN approach, including for models that cannot be fit using traditional likelihood-based methods. We further discuss our work in the context of the ongoing research leveraging simulation-based approaches to parameter estimation and model identification, and how these approaches broaden the class of cognitive models researchers can quantitatively investigate. Author summary: Computational cognitive models occupy an important position in cognitive science research, as they offer a simple way of quantifying cognitive processes (such as how fast someone learns, or how noisy they are in choice selection), and testing which cognitive theories offer a better explanation of the behavior. To relate cognitive models to the behavioral data, researchers rely on statistical tools that require estimating the likelihood of observed data under the assumptions of the cognitive model. This is, however, not possible to do for all models as some models present significant challenges to likelihood computation. In this work, we use artificial neural networks (ANNs) to bypass likelihood computation and approximation altogether, and demonstrate the success of this approach applied to model parameter estimation and model comparison. The proposed method is a contribution to ongoing development of modeling tools which will enable cognitive researchers to test a broader range of theories of cognition. [ABSTRACT FROM AUTHOR]

Quadrotor unmanned aerial vehicles (UAVs) are small, agile four-rotor systems suitable for various applications, from surveillance to disaster support missions. Hence, achieving high-precision trajectory tracking is crucial for their successful deployment. This research focuses on modelling, parameter identification, and Linear Quadratic Regulator (LQR) control design for quadrotors, aiming to enhance their trajectory following capabilities. The quadrotor dynamics are a sixth degree-of-freedom (6DOF) equation of motion derived from Newton's second law, encompassing moment of inertia, centre of gravity, weight, and thrust of propeller parameters. Experimental measurements are conducted to accurately determine these parameters, ensuring a realistic representation of the quadrotor system. Subsequently, a linearized model is constructed to provide a suitable plant for control system development. The LQR control design is intended to improve the trajectory tracking performance. This control strategy is validated through simulation and practical experiments, demonstrating its effectiveness in achieving high-precision trajectory following capabilites. The proposed approach demonstrates that LQR control effectively guides the quadrotor to resemble a predefined trajectory, experiencing only 3 % overshoot observed during the initial phase of flight. [ABSTRACT FROM AUTHOR]

The accurate estimation of the state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries is crucial for the safe and reliable operation of battery systems. In order to overcome the practical problems of low accuracy, slow convergence and insufficient robustness in the existing joint estimation algorithms of SOC and SOH, a Dual Adaptive Central Difference H-Infinity Filter algorithm is proposed. Firstly, the Forgetting Factor Recursive Least Squares (FFRLS) algorithm is employed for parameter identification, and an inner loop with multiple updates of the parameter estimation vector is added to improve the accuracy of parameter identification. Secondly, the capacity is selected as the characterization of SOH, and the open circuit voltage and capacity are used as the state variables for capacity estimation to improve its convergence speed. Meanwhile, considering the interaction between SOC and SOH, the state space equations of SOC and SOH estimation are established. Moreover, the proposed algorithm introduces a robust discrete H-infinity filter equation to improve the measurement update on the basis of the central differential Kalman filter with good accuracy, and combines the Sage–Husa adaptive filter to achieve the joint estimation of SOC and SOH. Finally, under Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Test (HWFET) conditions, the SOC estimation errors are 0.5% and 0.63%, and the SOH maximum estimation errors are 0.73% and 0.86%, indicating that the proposed algorithm has higher accuracy compared to the traditional algorithm. The experimental results at different initial values of capacity and SOC demonstrate that the proposed algorithm showcases enhanced convergence speed and robustness. [ABSTRACT FROM AUTHOR]

Reliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems only consider a single parameter in Gaussian noise. This paper develops a reliable identification scheme based on generalized multi-antenna time-frequency distribution (GMTFD) for MIMO systems with non-Gaussian interference and Gaussian noise. First, a new generalized correlation matrix is introduced to construct a generalized MTFD matrix. Then, the covariance matrix based on time-frequency distribution (CM-TF) is characterized by using the diagonal entries from the auto-source signal components and the non-diagonal entries from the cross-source signal components in the generalized MTFD matrix. Finally, by making use of the CM-TF, the Gerschgorin disk criterion is modified to estimate NTA, and the multiple signal classification (MUSIC) is exploited to estimate DOA for MIMO system. Simulation results indicate that the proposed scheme based on GMTFD has good robustness to non-Gaussian interference without prior information and that it can achieve high estimation accuracy and resolution at low and medium signal-to-noise ratios (SNRs). [ABSTRACT FROM AUTHOR]

Summary: By using the collected batch data and the iterative search, and based on the filtering identification idea, this article investigates and proposes a filtered multi‐innovation generalized projection‐based iterative identification method, a filtered generalized gradient‐based iterative identification method, a filtered generalized least squares‐based iterative identification method, a filtered multi‐innovation generalized gradient‐based iterative identification method and a filtered multi‐innovation generalized least squares‐based iterative identification method for equation‐error autoregressive systems described by the equation‐error autoregressive models. These filtered generalized iterative identification methods can be extended to other linear and nonlinear scalar and multivariable stochastic systems with colored noises. [ABSTRACT FROM AUTHOR]

The lumped element network model has been proven to be an efficient tool for the interpretation of transformer Frequency Response Analysis. However, it is challenging to obtain parameters of the model if transformer design information is unavailable. In such a case, optimisation algorithms can be used for gray‐box model parameter estimation. A methodology is developed by the authors to establish a transformer network model without winding design data, and instead, end‐to‐end open circuit Frequency Response and other terminal test results are utilised; and Genetic Algorithm is applied to approximate the unknown parameters of the model. The modelling approach developed is independent of the unit number of the network model and therefore guarantees the accuracy of optimisation. Furthermore, it can deal with transformers with a complicated winding structure such as interleaved disc type winding. Two single windings, helical and interleaved disc type, and a single‐phase 144/13 kV 60 MVA transformer are used to demonstrate the method. FRA spectra produced by the best estimated gray‐box model and the corresponding white‐box model are compared. The main features in amplitude and phase spectra are well matched, with low values of Relative Standard Deviation, for both single windings and transformer. Estimated electrical parameters show a high consistency with reference values, which are calculated based on winding design data. This validates the methodology and gives confidence to apply grey‐box modelling for FRA interpretation. [ABSTRACT FROM AUTHOR]

The photovoltaic (PV) system stands out as a viable energy source due to its environmental friendliness and cleanliness. The conversion rate at which solar power generation is still relatively low due to limitations imposed by advances in PV technology. For PV systems, an appropriate model with precise internal parameters is considerably more crucial to increase conversion efficiency further. Different PV mathematical models, such as single‐diode, two‐diode, and three‐diode, are available to model the PV system. Investigators are interested in assessing the accurate PV model parameters through the experimental voltage–current (I–V) samples or using the manufacturer's specifications. At the same time, the difficulty is in accurately assessing and developing a more trustworthy PV model with well‐optimized parameters. To address the parameter estimation of various solar PV models, in this article, a new bio‐inspired algorithm called Brownian random walk‐based Sand Cat Swarm Optimization Algorithm (SCSOA) named Boosted SCSOA (BSCSOA) is proposed and developed. Along with the Brownian random strategy, chaotic tent drift is also used to enhance the exploration and exploitation of SCSOA, and the proposed BSCSOA is applied to different models to estimate their parameters accurately. The effectiveness of the suggested BSCSOA is compared with other well‐known algorithms, including the basic SCSOA, in terms of statistical measures and fitness values. The obtained results demonstrated the superiority of the BSCSOA over the other algorithms for all PV models of the cell and module. [ABSTRACT FROM AUTHOR]

Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP. • New tool KETCHUP for kinetic model parameterization supports various formalisms. • Uses steady-state or time-series metabolomics, fluxomics and/or proteomics datasets. • Solution of least squares minimization problem employs interior-point solver, IPOPT. • KETCHUP affords both improved fit of datasets and recovery rate of best solution. • Kinetic model predictions demonstrate better performance than stoichiometric models. [ABSTRACT FROM AUTHOR]

This paper deals with two topics: roll predictions of marine vessels with machine-learning methods and parameter estimation of unknown ocean disturbances when the amplitude, frequency, offset, and phase are difficult to estimate. This paper aims to prevent the risky roll motions of marine vessels exposed to harsh circumstances. First of all, this study demonstrates complex dynamic phenomena by utilising a bifurcation diagram, Lyapunov exponents, and a Poincare section. Without any observers, an adaptive identification applies these four parameters to the globally exponential convergence using linear second-order filters and parameter estimation errors. Then, a backstepping controller is employed to make an exponential convergence of the state variables to zero. Finally, this work presents the prediction of roll motion using reservoir computing (RC). As a result, the RC process shows good performance for chaotic time series prediction in future states. Thus, the poor predictability of Lyapunov exponents may be overcome to a certain extent, with the help of machine learning. Numerical simulations validate the dynamic behaviour and the efficacy of the proposed scheme. [ABSTRACT FROM AUTHOR]

Purpose: Identifying unknown parameters has long been significant in the field of engineering, as they can be used for fault diagnosis and the development of numerical models. This study aims to determine the unknown parameters of mechanical models by applying deep neural networks (DNNs) using frequency–response functions (FRFs). Methods: The proposed network consists of several DNNs that estimate unknown parameters. The inputs of the DNNs requires the initial values of the unknowns. The estimated parameters by the DNNs were used to calculate the outputs. The magnitude and phase of the FRF obtained from an experiment were used as target data. To optimize the performance of the DNNs, Bayesian optimization is employed to search for appropriate weight factors in the loss function and hyperparameters in the DNNs. Additionally, the estimated parameters were adopted as the optimal parameters after the difference between the output and target data satisfied the stopping criterion. Results: The performance of the DNN-based method was confirmed for lumped and distributed parameter models with noise, which could significantly challenge the parameter identification. The proposed method showed high accuracy within 5% for both models and was validated in terms of convergence by simulations and experiments. Furthermore, we compared the proposed method with the conventional optimization methods across various noise levels and initial values. Conclusion: The proposed method successfully estimated unknown parameters under severe noise and initial conditions compared with the other methods. [ABSTRACT FROM AUTHOR]

Tasmanian devil optimization (TDO) algorithm represents one of the most recent optimization algorithms that were introduced based on the nature behavior of Tasmanian devil behavior. However, as a recent optimizer, its performance may provide inadequate balance among the exploitation and exploration abilities, especially when dealing with the multimodal and high-dimensional natures of optimization tasks. To overcome this shortage, a novel variant of the TDO, called improved Tasmanian devil optimization (ITDO), is introduced in this paper. In ITDO, two competitive strategies are embedded into TDO to enrich the scope of the searching capability with the aim of improving the diversification and identification of the algorithm. The effectiveness of the ITDO algorithm is examined by validating its performance on CEC 2020 benchmark functions with different landscape natures. The recorded results proved that the ITDO is very competitive with other counterparts. After ITDO exhibited a sufficient performance, then, it was applied to estimate the parameters of the 1 kVA, 230/230 V, single-phase transformer. Some assessment metrics along with convergence analysis are conducted to affirm the performance of the proposed algorithm. The recorded results confirm the competitive performance of the proposed method in comparison with the other optimization methods for the benchmark functions and can identify the accurate parameters for the single-phase transformer as the estimated parameters by ITDO are highly coincident with the experimental parameters. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC)‐based permanent magnet synchronous motor (PMSM) drive system has high dependence on parameter accuracy, while the traditional single observer architecture makes it difficult to solve the multi‐parameter observation problem. The authors propose the hybrid observer architecture for MPC in this paper. By constructing an adaptive parameter observer and a sliding mode observer (SMO) working in parallel, the variable exchange mechanism between the observers is designed and the current prediction model is reconstructed. The sensorless control of the PMSM under parameter uncertainty is achieved without affecting the dynamic and steady‐state performance. Compared with the single SMO architecture, the proposed algorithm improves the position identification precision under the uncertainty of the motor resistance and inductance, reducing the dependence of model parameters and improving the performance of sensorless MPC under parameter uncertainty. The experimental results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper applies the identified parameters of permanent magnet synchronous machines (PMSMs) for the maximum torque per ampere control (MTPA) under magnetic saturation. The variation in magnet flux with current is determined using a position offset approach while the variation in q-axis inductance with the current is estimated from the d-axis voltage equation afterward. In addition, the d-axis inductance is estimated at standstill by the injection of a small amplitude of high frequency d-axis current. The curve-fitted results of estimated parameters under different saturation conditions are then employed to aid the derivation of MTPA control law. The proposed method is experimentally verified on two prototype PMSMs. Experimental results show that compared with conventional MTPA schemes using fixed values of magnetic parameters, the proposed method can increase maximum output torque by 2.1% and 3.2% on two prototype PMSMs, respectively. [ABSTRACT FROM AUTHOR]

The parameter identification problem of photovoltaic (PV) models is classified as a complex nonlinear optimization problem that cannot be accurately solved by traditional techniques. Therefore, metaheuristic algorithms have been recently used to solve this problem due to their potential to approximate the optimal solution for several complicated optimization problems. Despite that, the existing metaheuristic algorithms still suffer from sluggish convergence rates and stagnation in local optima when applied to tackle this problem. Therefore, this study presents a new parameter estimation technique, namely HKOA, based on integrating the recently published Kepler optimization algorithm (KOA) with the ranking-based update and exploitation improvement mechanisms to accurately estimate the unknown parameters of the third-, single-, and double-diode models. The former mechanism aims at promoting the KOA's exploration operator to diminish getting stuck in local optima, while the latter mechanism is used to strengthen its exploitation operator to faster converge to the approximate solution. Both KOA and HKOA are validated using the RTC France solar cell and five PV modules, including Photowatt-PWP201, Ultra 85-P, Ultra 85-P, STP6-120/36, and STM6-40/36, to show their efficiency and stability. In addition, they are extensively compared to several optimization techniques to show their effectiveness. According to the experimental findings, HKOA is a strong alternative method for estimating the unknown parameters of PV models because it can yield substantially different and superior findings for the third-, single-, and double-diode models. [ABSTRACT FROM AUTHOR]

In this paper, three optimisation methods are used to determine the parameters of a commercial metal hydride hydrogen tank, considered as a grey box in which the thermophysical parameters are unknown. These parameters are essential for the development of energy management strategies and the study of tank ageing. A model of the hydride tank containing an AB 5 intermetallic was developed and experimentally validated. An absorption test campaign of the studied tank was carried out to collect data necessary for the optimisation algorithms. A comparative study was carried out in accordance with the speed of convergence and precision between the three stochastic optimisation methods: Particle Swarm Optimisation (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). The results of the simulation of the storage system using the parameters determined by the algorithms show a remarkably close agreement with the experimental data, with a maximum error of 2.1%. • Innovative proposal for the determination of parameters of a metal hydride hydrogen tank. • Experimental test for the static characterization of a metal hydride tank for hydrogen storage. • An experimentally verified mathematical model for the hydrogen storage system is put forth. • Validation with experimental data and three stochastic optimisation methods: PSO, GA and DE. [ABSTRACT FROM AUTHOR]

The electrochemical model of a proton exchange membrane (PEM) electrolyzer consists of a set of different unknown parameters, and identification of these parameters is needed. However, obtaining the optimum values of these parameters is critical, and this involves a complex, multi-variate, non-linear, and multimodal optimisation problem. In this article, a novel model parameter estimation problem for a PEM electrolyzer with eight unknown model parameters is formulated and solved using modified honey badger algorithm (MHBA). A mean squared error-based objective function is formulated and considered as the mean squared error (MSE) between the experimental voltage and the estimated voltage using MHBA algorithm. The accuracy of the proposed model is validated using polarization curves (J-V curve). It is found that the MHBA outperforms with a good mapping between experimental and estimated voltage. Also, the reliability and robustness for parameter estimation algorithm is validated considering two case studies with four different operating conditions. Additionally, the results obtained using MHBA are compared to those obtained with other competing algorithms. A statistical study including different statistical indices is also carried out to prove the robustness for the proposed approach. The results show that the minimum obtained values of MSE for different operating conditions are 8.73E-06, 8.75E-06, 6.17E-05 and 6.44E-05. Box plot study and convergence curves have also been drawn to check the effectiveness of the algorithm. Furthermore, to prove the superiority of the proposed approach, the PEM electrolyzer's performance is explored at different operating conditions, such as operating temperatures and output hydrogen pressures. • Proposing a modified honey badger algorithm (MHBA) for parameter estimation of PEM electrolyzer. • Accuracy and reliability of MHBA is validated using two case studies through polarization characteristics. • Statistical study and box plot analysis is performed to prove superiority of MHBA over other optimisation algorithms. • Effect of different temperatures and pressures is analyzed on electrolyzer performance. [ABSTRACT FROM AUTHOR]

This paper focuses on iterative parameter estimation methods for a nonlinear closed-loop system (i.e., a nonlinear feedback system) with an equation-error model for the open-loop part. By applying negative gradient search, a gradient-based iterative algorithm is constructed. To reduce the computational costs and improve the parameter estimation accuracy, the hierarchical identification principle is employed to derive a hierarchical gradient-based iterative algorithm. A simulation example is provided to test the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Summary: This article proposes a novel identification framework for estimating the parameters of the controlled autoregressive autoregressive moving average (CARARMA) models with colored noise. By means of building an auxiliary model and using the hierarchical identification principle, this article investigates a highly‐efficient parameter estimation algorithm. In order to meet the need for identifying the systems with large‐scale parameters, the whole parameters of the CARARMA system is separated into two parameter sets and a decomposition and composition recursive identification algorithm (i.e., hierarchical generalized extended least squares algorithm or decomposition‐based recursive generalized extended least squares algorithm) is presented. Moreover, this article analyzes the convergence of the proposed decomposition and composition recursive identification algorithm. The performance analysis shows that the proposed decomposition and composition identification algorithm can reduce the complexity of the identification algorithm compared with the algorithm without decomposition. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties. First, considering the uncertainty of structural and aerodynamic parameters, the six-degree-of-freedom (6DoF) nonlinear equations describing the position and attitude dynamics of the rotor-missile are established, respectively, in the inertial and body-fixed reference frames. Next, a hierarchical adaptive trajectory tracking controller that can guarantee closed-loop stability is proposed according to the cascade characteristics of the 6DoF dynamics. Then, a memory-augmented update rule of unknown parameters is proposed by integrating all historical data of the regression matrix. As long as the finitely excited condition is satisfied, the precise identification of unknown parameters can be achieved. Finally, the validity of the proposed trajectory tracking controller and the parameter identification method is proved through Lyapunov stability theory and numerical simulations. [ABSTRACT FROM AUTHOR]

Identifying an accurate dynamics model remains challenging for humanoid robots. The difficulty is mainly due to the following two points. First, a good initial model is required to evaluate the feasibility of motions for data acquisition. Second, a highly nonlinear optimization problem needs to be solved to design movements to acquire the identification data. To cope with the first point, in this paper, we propose a curriculum of identification to gradually learn an accurate dynamics model from an unreliable initial model. For the second point, we propose using a large-scale human motion database to efficiently design the humanoid movements for the parameter identification. The contribution of our study is developing a humanoid identification method that does not require the good initial model and does not need to solve the highly nonlinear optimization problem. We showed that our curriculum-based approach was able to more efficiently identify humanoid model parameters than a method thatjust randomly picked reference motions for identification. We evaluated our proposed method in a simulation experiment and demonstrated that our curriculum was led to obtain a wide variety of motion data for efficient parameter estimation. Consequently, our approach successfully identified an accurate model of an 18-DoF, simulated upper-body humanoid robot. [ABSTRACT FROM AUTHOR]

To address the problem of poor robustness and accuracy of vehicle state and parameter estimation by conventional Kalman filter in the non-Gaussian environments, a three-degree-of-freedom vehicle model with an improved Dugoff tire model is established and a joint estimator of vehicle state and parameter is designed using the Maximum Correntropy (MC) adaptive unscented Kalman filter (AUKF) algorithm in order to simultaneously estimate and identify the yaw rate, longitudinal vehicle speed, lateral vehicle speed, vehicle mass and rotational inertia. The proposed joint estimator algorithm was validated by Simulink/CarSim simulation testbed under Double Lane Change and Sine Wave Steering Input conditions. The results show that MC combined with AUKF (MCAUKF) algorithm has higher estimation accuracy and better convergence compared to the unscented Kalman filter (UKF) and the MC combined with UKF (MCUKF) in non-Gaussian environments, and the MCAUKF estimator is more suitable for state estimation and parameter identification of real vehicles. [ABSTRACT FROM AUTHOR]

Extensive research has been conducted on models of ordinary differential equations (ODEs), yet these deterministic models often fail to capture the intricate complexities of real-world systems adequately. Thus, many studies have proposed the integration of Markov chains into nonlinear dynamical systems to account for perturbations arising from environmental changes and random variations. Notably, the field of parameter estimation for ODEs incorporating Markov chains still needs to be explored, creating a significant research gap. Therefore, the objective of this study is to investigate a comprehensive model capable of encompassing real-life scenarios. This model combines a system of ODEs with a continuous-time Markov chain, enabling the representation of a continuous system with discrete parameter switching. We present a machine discovery framework for parameter estimation in nonlinear dynamical systems with Markovian switching, effectively addressing this research gap. By incorporating Markov chains into the model, we adeptly capture the time-varying dynamics of real-life systems influenced by environmental factors. This approach enhances the applicability and realism of the research, enabling more precise representations of dynamical systems with Markovian switching in complex scenarios. [ABSTRACT FROM AUTHOR]

This paper deals with parameter estimation problem of large‐scale systems. A recursive distributed parameter estimation algorithm, based on the minimization of the prediction estimation error method, is developed. Specifically, the class of large‐scale systems that are composed of several interconnected sub‐systems is considered. Each interconnected sub‐system is modelled by a linear discrete‐time state space mathematical model with unknown parameters. The convergence analysis is then achieved using the Lyapunov approach. The theoretical analysis and simulation results prove the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To ensure the safe operation of electric vehicles (EVs), it is essential to estimate the internal status of lithium-ion batteries online. When current sensors are faulty, current measurement offset (CMO) interference occurs, and traditional state estimation algorithms become invalid due to incorrect current data. In this paper, a two-stage forgetting factor recursive least squares (FFRLS) algorithm is proposed for online identification of battery parameters and estimation of the CMO. Afterwards, a joint estimation framework is established to obtain the state of charge (SOC) and capacity with adaptive extended Kalman filter (AEKF) and iterative reweighted least squares (IRLS) algorithms, respectively. The open-source dataset of the CALCE Battery Research Group is used to verify the accuracy and robustness of the algorithm. The results show that the mean absolute error (MAE) of the CMO online estimation is less than 2.5 mA, the mean absolute percentage error (MAPE) of the SOC estimation is less than 2%, and the error in estimating the usable capacity is less than 2.5%. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study that explores the performance of various meta-heuristics employed for Optimal Signal Design, specifically focusing on estimating parameters in nonlinear systems. The study introduces the Robust Sub-Optimal Excitation Signal Generation and Optimal Parameter Estimation (rSOESGOPE) methodology, which is originally derived from the well-known Particle Swarm Optimization (PSO) algorithm. Through a real-life case study involving an Autonomous Surface Vessel (ASV) equipped with three Degrees of Freedom (DoFs) and an aerial holonomic propulsion system, the effectiveness of different meta-heuristics is thoroughly evaluated. By conducting an in-depth analysis and comparison of the obtained results from the diverse meta-heuristics, this study offers valuable insights for selecting the most suitable optimization technique for parameter estimation in nonlinear systems. Researchers and experimental tests in the field can benefit from the comprehensive examination of these techniques, aiding them in making informed decisions about the optimal approach for optimizing parameter estimation in nonlinear systems. [ABSTRACT FROM AUTHOR]

In biomedical research, identifying genes associated with diseases is of paramount importance. However, only a small fraction of genes are related to specific diseases among the multitude of genes. Therefore, gene selection and estimation are necessary, and the accelerated failure time model is often used to address such issues. Hence, this article presents a method for structural identification and parameter estimation based on a non-parametric additive accelerated failure time model for censored data. Regularized estimation and variable selection are achieved using the Group MCP penalty method. The non-parametric component of the model is approximated using B-spline basis functions, and a group coordinate descent algorithm is employed for model solving. This approach effectively identifies both linear and nonlinear factors in the model. The Group MCP penalty estimation exhibits consistency and oracle properties under regularization conditions, meaning that the selected variable set tends to have a probability of approaching 1 and asymptotically includes the actual predictive factors. Numerical simulations and a lung cancer data analysis demonstrate that the Group MCP method outperforms the Group Lasso method in terms of predictive performance, with the proposed algorithm showing faster convergence rates. [ABSTRACT FROM AUTHOR]

Accurate parameter estimation is crucial and challenging for the design and modeling of PV cells/modules. However, the high degree of non-linearity of the typical I–V characteristic further complicates this task. Consequently, significant research interest has been generated in recent years. Currently, this trend has been marked by a noteworthy acceleration, mainly due to the rise of swarm intelligence and the rapid progress of computer technology. This paper proposes a developed Mountain Gazelle Optimizer (MGO) to generate the best values of the unknown parameters of PV generation units. The MGO mimics the social life and hierarchy of mountain gazelles in the wild. The MGO was compared with well-recognized recent algorithms, which were the Grey Wolf Optimizer (GWO), the Squirrel Search Algorithm (SSA), the Differential Evolution (DE) algorithm, the Bat–Artificial Bee Colony Optimizer (BABCO), the Bat Algorithm (BA), Multiswarm Spiral Leader Particle Swarm Optimization (M-SLPSO), the Guaranteed Convergence Particle Swarm Optimization algorithm (GCPSO), Triple-Phase Teaching–Learning-Based Optimization (TPTLBO), the Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO), the quasi-Opposition-Based Learning Whale Optimization Algorithm (OBLWOA), and the Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO). The experimental findings and statistical studies proved that the MGO outperformed the competing techniques in identifying the parameters of the Single-Diode Model (SDM) and the Double-Diode Model (DDM) PV models of Photowatt-PWP201 (polycrystalline) and STM6-40/36 (monocrystalline). The RMSEs of the MGO on the SDM and the DDM of Photowatt-PWP201 and STM6-40/36 were 2.042717 × 10 − 3 , 1.387641 × 10 − 3 , 1.719946 × 10 − 3 , and 1.686104 × 10 − 3 , respectively. Overall, the identified results highlighted that the MGO-based approach featured a fast processing time and steady convergence while retaining a high level of accuracy in the achieved solution. [ABSTRACT FROM AUTHOR]

Multifunctional materials have tremendous potential for engineering applications as they are able to convert mechanical to electromagnetic energy and vice-versa. One of the features of this class of materials is that they show significant hysteresis, which needs to be modeled correctly in order to maximize their application potential. A method of modeling multifunctional materials that exhibit the phenomenon of hysteresis and is compatible with the laws of thermodynamics was developed recently. The model is based on the Preisach hysteresis operator and its storage function and may be interpreted as a two-input, two-output neural net with elementary hysteresis operators as the neurons. The difficulty is that the parameters in the model appear in a non-linear fashion, and there are several constraints that must be satisfied by the parameters for thermodynamic compatibility. In this article, we present a novel methodology that uses the rate-independent memory evolution properties of the Preisach operator to split the parameter estimation problem into three numerically well-conditioned, linear least squares problems with constraints. The alternative direction method of multipliers (ADMM) algorithm and accelerated proximal gradient method are used to compute the Preisach weights. Numerical results are presented over data collected from experiments on a Galfenol sample. We show that the model is able to fit not only experimental data for strain and magnetization over a wide range of magnetic fields and stress but also able to predict the response for stress and magnetic fields not used in the parameter estimation. [ABSTRACT FROM AUTHOR]

In this article, the parameter identification problem of the output error model is investigated under the application requirement of online parameter identification of a two‐degree‐of‐freedom servo turntable system. To eliminate the influence of colored noise in the observed output signal of the output error model on the algorithm identification accuracy, the momentum factor is introduced into the innovation term of the recursive least squares algorithm, and the momentum‐innovation recursive least squares (MI‐RLS) algorithm is proposed. Further, to improve the convergence speed and identification accuracy of the algorithm while avoiding increasing the algorithm complexity significantly, a reframed multi‐innovation strategy is introduced, and a momentum reframed multi‐innovation least squares (MR‐MILS) algorithm is developed. After analyzing the complexity of the proposed algorithms, the convergence performances of the two algorithms are verified using the theory of martingale convergence, and the results show that the reframed multi‐innovation strategy can accelerate the convergence speed of the MR‐MILS algorithm. The effectiveness of the proposed identification approach is demonstrated via simulation results. [ABSTRACT FROM AUTHOR]

Rice blast disease, caused by the fungus Magnaporthe oryzae, poses a significant threat to rice cultivation. One effective way to deal with this disease is to identify and introduce resistant varieties using different breeding methods. This study utilized a population of 153 recombinant inbred lines (RILs) derived from the crossing of the Shahpasand (SH) and IR28 varieties, characterized by susceptibility and resistance to leaf blast, respectively. In combination with 12 control varieties, these genotypes were subjected to an extensive evaluation of disease severity (5 stages), the area under the disease progress curve (AUDPC), type, and the infection rate in 2021 and 2022. Analysis of variance revealed significant genetic variation, highlighting the potential of the RIL population for identifying and selecting resistant lines. Employing cluster analysis and the multi-trait genotype-ideotype distance index (MGIDI), 17 lines were identified as the most resistant over a two-year evaluation period. The average AUDPC for these resistant lines was estimated at 2.435 ± 0.114, and lines 17 and 111 had the lowest AUDPC (1.526 and 1.630, respectively) and showed the least infection in two years. Conversely, lines 42 and 43 showed the highest AUDPC values (255.312 and 248.209) along with heightened sensitivity. The use of MGIDI yielded a substantial selection differential (SD) of −59.12% for traits related to leaf blast disease resistance, demonstrating the effectiveness of this method. Furthermore, new recombinant populations are expected to be developed in future plant breeding projects by crossing the most susceptible and resistant lines, which will be new sources of resistance to this disease. [ABSTRACT FROM AUTHOR]