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A parameter identification method based on genetic algorithm (GA) is presented to solve the multimodel parameters identification problem for the main steam temperature of ultra-supercritical (USC) units. Linear ranking selection, nonuniform linear crossover, and Gaussian mutation are employed in the algorithm design to enhance the convergence speed and the accuracy of the identification. Besides, the uniform design method is executed to initialize the population and the sigmoid function with adaptation is applied to adjust the probabilities of crossover and mutation. Simulations carried out with the field operation data from Haimen USC units, including two processes-parameters identification and model verification. The simulation results show that the improved genetic algorithm performs well in global parameters searching and the proposed identification methodology offers good results for the multimodel parameters identification. [ABSTRACT FROM AUTHOR]

Highlights: [•] Building envelope integrated with double layers SSPCMs. [•] Simplified dynamic model consisting of one 3R2C model and two 4R2C models. [•] Parameters identified by genetic algorithm estimators. [•] The simplified model is reliable compared with theoretical model. [•] The new wall system is for year-round application. [ABSTRACT FROM AUTHOR]

In this paper the identification of diffusion coefficient, retardation factor and surface distribution coefficient for selected salts in poly(vinyl alcohol) hydrogels is performed. The identification of the transport parameters is based on the previously developed inverse problem technique using experimental data from the reservoir test and the solution of the diffusive transport equation with linear equilibrium sorption. The estimated values of diffusion coefficient are: for physiological fluid (6.30±0.10)×10−10 m2/s, for 1M NaCl (6.42±0.39)×10−10 m2/s, and for 1M KCl (7.94±0.38)×10−10 m2/s. The retardation factor for all tested materials and salts is equal or close to one. The average value of the effective surface distribution coefficient is equal to 0.5. [ABSTRACT FROM AUTHOR]

An assistive robot is a novel service robot, playing an important role in the society. For instance, it can amplify human power not only for the elderly and disabled to recover/rehabilitate their lost/impaired musculoskeletal functions but also for healthy people to perform tasks requiring large forces. Consequently, it is required to consider both accurate position control and human safety, which is the compliance. This paper deals with the robot control compliance problem based on the QNX real-time operating system. Firstly, the mechanical structure of a compliant joint on the assistive robot is designed using Solidworks. Then the parameters of the assistive robot system are identified. The software of robot control includes data acquisition and processing, and control to meet the compliance requirement of the joint control. Finally, a Hogan impedance control experiment is carried out. The experimental results prove the effectiveness of the method proposed. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we investigate the synchronization and parameter identification of chaotic system with unknown parameters and mixed delays. A new approach is proposed for designing a controller and a update rule of unknown parameters based on a special matrix structure, and the synchronization and the parameter identification are realized under the controller and the update rule. Numerical simulations are carried out to confirm the effectiveness of the approach. A significant advantage is that the process of designing a controller and a update rule become very clear and easy by the proposed approach. [Copyright &y& Elsevier]

Gurson–Tvergaard–Needleman model is widely used to describe the three stages of ductile tearing: nucleation, growth and the coalescence of micro-voids. The aim of this article is to study the relationship between volume fraction of voids and the fracture strain ɛf. The effects of the volume fraction of nucleation, fN, and the critical volume fraction, fc, were analysed. These parameters play crucial roles in the process of ductile damage. A phenomenological analysis is carried out to study the relationship between the different void volume parameters and the fracture strain ɛf. A method is proposed for the determination of fN and fc, knowing the experimental fracture strain ɛf. The experimental parameters are extracted from the load–diametric contraction curve of an axisymmetric notched tensile bar test AN2. [ABSTRACT FROM PUBLISHER]

Sensorless control allows lower cost and higher reliability of the induction machine "IM" drives. The performance of an IM drive remains dependent on the rapidity and the accuracy of the submitted parameters values. In electric vehicle "EV" applications, the IM parameters may widely vary owing to the external disturbances and the snippy control variations. Some solutions are already available, such as intelligent controllers, but they still need to improve their reactivity and their accuracy level. Nowadays, the finite element methods provide very accurate information on electrical machines. In this paper an IM finite element model "IMFEM" is developed using the finite element method magnetics numerical tool "FEMM" to identify the IM parameters. Practice tests are carried out and results are compared to those obtained from simulation. The IMFEM is then integrated in a complete EV power train model using an IM observer. Trials are performed and results are compared with those obtained by the EV power train model using an IM constant parameters controller. [ABSTRACT FROM AUTHOR]

• A hybrid experimental-numerical strategy to predict ductile fracture is proposed. • The post-necking strain behaviour and ductility diagram parameters are identified. • Only two simple experiments and three numerical optimisations are required. • The proposed strategy was validated by comparing experiments and predictions. Although various effective models for simulating ductile fracture in metallic materials have been established, the methods to identify the required parameters have scarcely been clarified. The difficulty for parameters identification of ductile fracture models is the actual bottleneck for accurate numerical predictions in ductile fracture simulation. In this paper, a strategy to efficiently identify the post-necking strain behaviour and the ductility diagram parameters is proposed by a hybrid experimental-numerical procedure. In the proposed strategy, only two types of experiments are required: a conventional tensile test and a crack growth test. All the parameters to simulate the post-necking strain hardening behaviour and the ductile fracture can be uniquely identified via numerical optimisations using load-displacement curves of the two tests. The proposed strategy was applied to an A2024T3 aluminium alloy and the post-necking hardening and ductility diagram parameters were identified by a flat tensile test and an Arcan test. Two types of plate specimens with a hole were then used to validate the proposed strategy. Good agreements between the experimental results and the numerical predictions clearly show the feasibility and accuracy of the proposed strategy. The results demonstrated that the proposed strategy is simple but effective. Therefore, it can be a general basis for characterising mechanical properties of various metallic materials. [Display omitted] [ABSTRACT FROM AUTHOR]

After avoiding the Standard Test Condition (STC) related to the manufacturer of PV cells, accurate solar system modeling is considered the most important task to study this system. Several mathematical developments based-approaches or algorithms were considered valuable tools to parameterize the electrical circuit of the photovoltaic models. Thus, these parameters are well determined using optimization algorithms. To achieve this goal, an experimental database or manufacturer's datasheet is requested. This paper proposes a novel Improved Arithmetic Optimization Algorithm (IAOA) to extract the solar cell/panel parameters. Also, practical tests are presented for obtaining the measured illustration of the I-V and P-V characteristics. Thus, the Double Diode Model (DDM) of the electrical circuit of PV panel is studied considering various statistical analyzes, to know the Minimum (Min Fobj,DDM), the Maximum (Max Fobj,DDM), the Average (Ave Fobj,DDM), and the Standard Deviation (SD Fobj,DDM). The estimated I-V and P-V curves using IAOA is compared with eight other recently published algorithms. In addition, the evaluation of the proposed IAOA proves that this proposal has a satisfactory performance than all tested algorithms. The obtained results show that the IAOA is considered first in terms of effectiveness and accuracy by 9.537e−06 of standard deviation. Moreover, considering the variability of operating conditions, the proposed IAOA shows its high superiority to optimize the electrical parameters of the solar cell TDM Where it has the lowest errors by 3.623e−05 of RMSE, 4.183e−06 of MAE, and 1.397e−05 of RMSD. [ABSTRACT FROM AUTHOR]

In the field of ship deformation measurement, the inertial matching method has become a current research hotspot due to its advantages of high accuracy, easy implementation, and strong real-time performance. However, the parameters of dynamic deformation are affected by the environment, which severely restricts the further improvement of its accuracy. In response to this problem, this paper derives the mapping mechanism between the angular velocity difference and the dynamic deformation angle. And the data in the sliding window is used to establish the objective function on this basis. After that an improved whale optimization algorithm (IWOA) based on logic mapping is designed to perform parameters identification. Simulation and turntable experiment results show that the algorithm proposed in this paper can effectively identify the parameters. The results of the shipboard experiment show that the measurement accuracy of the deformation angle is significantly improved after parameters identification, and the RMS of estimation errors for the pitch, roll and yaw are 10. 0 4 ′ ′ , 12. 1 6 ′ ′ , 9. 0 7 ′ ′ respectively. • The mapping relationship between the angular velocity difference and the dynamic deformation angle is analyzed. • The optimal function for parameter identification is established. • Improved WOA based on logistic mapping was introduced to obtain parameters. • Simulations, turntable and shipboard experiments are carried out. [ABSTRACT FROM AUTHOR]

A theoretical model describing the relaxation of charge carriers in semiconductors of high resistance under the influence of the laser pulses is presented. It is demonstrated that parameters of the trapping states relevant to the processes of the conductivity relaxation can be defined by fitting the experimental data. Time evolution of the conductivity of the GaAs bulk semiconductor under the influence of nanosecond and picosecond laser pulses is considered. Effect of two laser pulses, when the first one results in population of the trapping state and the second one induces its depopulation, is also considered. First Published Online: 14 Oct 2010

Highlights: [•] Identification of Van Der Pol–Duffing oscillators. [•] Contraction techniques for searching area. [•] Re-initialization strategy on number theory. [•] A novel ABC with differential evolution operators. [ABSTRACT FROM AUTHOR]

In the preliminary design stage of high-speed train smart suspension, a simple, yet accurate magnetorheological (MR) damper model whose parameters have clear physical meaning is needed. Based on the working mechanism analysis and the dynamic behavior study of the MR damper, a new consecutive viscoelastic plastics (VEP) model is proposed. A methodology to find the parameters of the proposed model directly has been proposed. The comparison with experimental results indicates that the proposed model could adequately characterize the intrinsic nonlinear behavior of the MR damper, including the hysteretic behavior, roll-off phenomenon, and the variation of the hysteresis width in terms of the frequency and magnitude of excitation. The results of experimental testing prove that the accuracy of the proposed model is higher than that of the phenomenological model while only containing four undetermined parameters with clear physical meaning. Moreover, based on the proposed VEP model, a nonlinear stiffness VEP (nkVEP) model is developed with higher precision in the hysteretic region. The nkVEP model, which can reproduce the behavior of the damper with fluctuating input current, is developed. The proposed model could predict accurately the response of the MR damper in a wide range of frequency and displacement. [ABSTRACT FROM AUTHOR]

Most of the existing results for identifying parameters in dynamical systems through adaptive coupling techniques always require a uniform Lipschitz condition on system vector fields. However, it is not this requirement but the only locally Lipschitz condition that is more suitable for many nonlinear systems in real applications. Also time delays sometimes are essential for modeling real systems, so that in addition to the discrete form, time delays in the distributed form could be considered. This paper thus considers the problem of parameters identification in only locally Lipschitzian dynamical systems with multiple types of time delays through adaptive coupling techniques. In particular, the paper proposes some linear independence conditions for realization of parameters identification in both nonautonomous and autonomous systems and draws a comparison between these conditions and the persistent of excitation condition which is used frequently in the literature. Moreover, the paper proposes an index to determine the nonlinear degree of the considered systems, which enables us to distinguish the systems in which the parameters identification can be globally or only locally achieved. The paper also uses some representative and elaborate examples to illustrate the practical usefulness of the obtained theoretical results. During the discussion, the paper takes into full consideration the property of infinite dimensions for nonautonomous or autonomous dynamical systems with time delays. [ABSTRACT FROM AUTHOR]

Abstract: The use of HSLA steels for the manufacture of automotive components is interesting from an engineering point of view. This family of steels, while possessing high strength, also has good formability and can be used in forming manufacturing processes. In some forming processes such as blanking, shear strain localization occurs, which causes damage and results in the final fracture of the material. This paper presents an experimental study based on in situ tests to understand and identify the physical mechanisms of ductile damage under two stress states: tension and shear. Different macroscopic tests were performed to calibrate a damage model based on a micromechanical approach. This damage model is based on the Gurson–Tvergaard–Needleman theory and presents recent improvements proposed by Nahshon and Hutchinson and by Nielsen and Tvergaard so as to better predict fracture under a wide range of stress states, especially with low levels of stress triaxiality. These extensions have made the identification of the material parameter more complicated. In this work an identification strategy has been proposed using tests on specimens with different shapes. The identified parameter values are validated and the fracture model show good predictive capability over a wide stress state range. [Copyright &y& Elsevier]

A high precision fast projective synchronization method for chaotic systems with unknown parameters was proposed by introducing optimal matrix. Numerical simulations indicate that the precision be improved about three orders compared with other common methods under the same condition of software and hardware. Moreover, when average error is less than 10-3, the synchronization speed is 6500 times than common methods, the iteration needs only 4 times. The unknown parameters also were identified rapidly. The theoretical analysis and proof also were given. [ABSTRACT FROM AUTHOR]

Dynamic behavior of spindle-holder taper joint is critical in the spindle system of machine tool, related to the machining process. The parameters of the holder joint are significant for the spindle-holder system. This paper developed a new approach to investigate the dynamic behavior of the taper joint in modeling and parameters identification. Firstly, dynamic model is illustrated in both radial and axial directions based on the pairs of spring and damping. And frequency response function is used to identify the unknown modal parameters of frequencies and damping ratios. Secondly, a platform is designed and manufactured to establish the testing system in order to investigate the taper joint. Furthermore, taper joint of a BT50 holder is investigated, and the pre-force at the end of holder is the main consideration during the testing. The results show that the dynamic stiffness increases with the increasing of pre-force at the end of the holder. In addition, the paper discusses the phenomenon in microstructure based on peak-hollows model and the joint parameters are proved to be workable through simulation. The joint parameters are instructive to the design and manufacturing of the spindle. [ABSTRACT FROM AUTHOR]

Determination of geomechanical parameters of petroleum reservoir and surrounding rock is important for coupled reservoir-geomechanical modeling, borehole stability analysis and hydraulic fracturing design. A displacement back analysis technique based on artificial neural network (ANN) and genetic algorithm (GA) combination is investigated in this paper to identify reservoir geomechanical parameters based on ground surface displacements. An ANN is used to map the nonlinear relationship between Young's modulus, E, Poisson's ratio, v, internal friction angle, Φ, cohesion, c and ground surface displacements. The necessary training and testing samples for ANN are created by using numerical analysis. GA is used to search the set of unknown reservoir geomechanical parameters. Results of the numerical experiment show that the displacement back analysis technique based on ANN-GA combination can effectively identify reservoir geomechanical parameters based on ground surface movements as a result of oil and gas production. [ABSTRACT FROM AUTHOR]

Abstract: In the present study a procedure to characterize the finite viscoelasticity and to simultaneously identify the influence of adhesion in nanoindentation experiments of soft polymers is developed. Silicone rubber, which is assumed to be an isotropic elastomer, is chosen to be examined. Different nanoindentation testing protocols are used to visualize and proof the viscoelastic properties and the adhesion behavior of a soft silicone rubber in contact with a Berkovich tip. It could be shown that the analytical solution of linear viscoelastic indentation has some limitations in order to predict the experimental data that contains finite viscoelasticity as well as adhesion effects. The inverse method is applied by using the finite element computation combined with a numerical optimization subroutine. A viscoelastic model at finite strain is chosen to represent the silicone rubber’s behavior. The default contact pressure–clearance relationship used in ABAQUS® is modified; a surface-based adhesive behavior in traction–separation law is incorporated into the contact pairs. The real geometry of the Berkovich tip is considered in order to minimize the systematic errors between the numerical model and the experiments. Finally, the parameters of the chosen viscoelastic constitutive model and the adhesive contact model are identified by matching the response of the numerical model with the experimental force–displacement curves. The present model contains the surface adhesion and is verified to show better reproducibility regarding the experiments than the analytical solution. There are several drawbacks of the analytical solution that are also presented in this work. [Copyright &y& Elsevier]

With the improvement of the aircraft flight performance and development of computing science, uninhabited combat aerial vehicle (UCAV) could accomplish more complex tasks. But this also put forward stricter requirements for the flight control system, which are the crucial issues of the whole UCAV system design. This paper proposes a novel UCAV flight controller parameters identification method, which is based on predator-prey particle swarm optimization (PSO) algorithm. A series of comparative experimental results verify the feasibility and effectiveness of our proposed approach in this paper, and a predator-prey PSO-based software platform for UCAV controller design is also developed. [ABSTRACT FROM AUTHOR]

In this study, we present a systematic scheme to identify the material parameters in constitutive model of hyperelastic materials such as rubber. This approach is proposed based on the combined use of general regression neural network, experimental data and finite element analysis. In detail, the finite element analysis is carried out to provide the learning samples of GRNN model, while the results observed from the uniaxial tensile test is set as the target value of GRNN model. A problem involving parameters identification of silicone rubber material is described for validation. The results show that the proposed GRNN-based approach has the characteristics of high universality and good precision, and can be extended to parameters identification of complex rubber-like hyperelastic material constitutive.

This paper discusses how to identify tire model coefficients that are used to compare longitudinal forces when braking. Those in the automotive world have worked extremely hard to obtain these parameters and different methods have been used to match the values of these parameters. This paper proposes the use of self-organizing maps to tackle this problem whereby interactively searches are carried out to find the optimum tire model parameters. The objective of this research is to prove the capability of self-organizing maps (SOMs) to classify a vehicle's braking formula on a roller brake tester from the MOT (Ministry of Transport) and on flat ground. The neural network produced a good brake-slip ratio when presented with data that are not used in network training. This means that the methodology is feasible. This tool easily obtains the brake-slip equation of each experiment and the braking on two different experimental tests will be compared. [ABSTRACT FROM AUTHOR]

This article presents statistical model parameter identification using Bayesian inference when parameters are correlated and observed data have noise and bias. The method is explained using the Paris model that describes crack growth in a plate under mode I loading. It is assumed that the observed data are obtained through structural health monitoring systems, which may have random noise and deterministic bias. It was found that a strong correlation exists (a) between two parameters of the Paris model, and (b) between initially measured crack size and bias. As the level of noise increases, the Bayesian inference was not able to identify the correlated parameters. However, the remaining useful life was predicted accurately because the identification errors in correlated parameters were compensated by each other. It was also found that the Bayesian identification process converges slowly when the level of noise is high. [ABSTRACT FROM PUBLISHER]

To discern false modes during experimental modal analysis process and identify dense modes in axial symmetrical structures, the dynamic properties of truncated conic aluminum alloy thin shell satellite adaptor was experimental investigated. Electromagnetic vibration generators were used for exciting structures, and piezoelectric accelerometers for measuring the corresponding response. Polynomial method was applied for fitting experimental frequency response functions and identifying modal parameters. The effects of different methods between single input single output (SISO), single input multiple output (SIMO), and multiple input multiple output (MIMO) were evaluated systematically, both experimental and numerical analysis results were compared to recognize false modes. The experimental results show that the MIMO method helps in splitting dense modes in axial symmetry structures, and the former 10 modes of satellite adaptor were clearly identified. However, improper exciter fixation results in false modes in frequency response function. For experimental modal analysis of lightweight structures, special attention should be paid on additional mass and stiffness due to exciter fixation. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the dynamic parameters of the valve test system are identified based on a novel adaptive logistic chaotic particle swarm optimization. Three new aspects are considered in the new identification algorithm to enhance global search capability and improve identification accuracy: first, modifying particle formula, second, adding chaos to the iterative process, third, adjusting inertia weight and acceleration coefficients adaptively in the particle equations. The xperimental results demonstrate that the adaptive logistic chaotic particle swarm optimization algorithm is more effective to apply for parameters identification than other algorithms. [Copyright &y& Elsevier]

Abstract: Wind Energy is a very promising energy for the future. It is well known that the power delivered by wind turbines directly coupled to the grid is not constant as a result of the wind variability. In the absence of storage systems, a fluctuating power supply produced, can lead to voltage variations in the grid and flicker. Another disadvantage of most induction machines utilized in the wind turbines is that the required reactive power varies with wind speed and time. These problems can make the use of double fed induction generators attractive for wind turbine applications. Doubly-fed induction machines (DFIMs) are beginning to dominate the wind generation market, particularly for the larger sizes of turbine. This work is dedicated to the identification of the parametric double-fed induction machine. We propose a model of the DFIG based on the method of vector space. This model is used to validate the experimental results of identified parameters of the machine. After considering several methods of parameter identification of induction machines, provided with the results of the experiments, we are particularly interested in standardized testing. The proposed approach allows determining the electrical parameters of the machine using conventional methods static and dynamic, mechanical parameters are estimated using a digital channel, following the curve of smoothed experimental slowdown. The identified model parameters are verified by comparing their simulated stator and rotor currents responses against the measured currents. It is again observed that the estimated model responses match the measured responses well. [Copyright &y& Elsevier]

This paper proposes a voltage support scheme for traction networks, which compensates for the voltage drop along the catenary line to which locomotives are connected. The proposed method is based on the injection of capacitive reactive power through the current controlled active line-side converter of locomotives. Comparing the catenary voltage with its reference value, the error is fed to a gain-scheduled PI-controller. The controller generates the q-axis reference value of the converter current, which is responsible for reactive power injection. The gain scheduling is carried out through identifying the parameters of the catenary line. The catenary parameters identification is performed by harmonic current injection and analyzing its impact on the catenary voltage. The performance of the proposed control strategy is evaluated in MATLAB/PLECS environment for a traction network consisting of one locomotive and two substations and moreover, for a two-locomotive three-substation traction network. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a new method for an online electric parameters identification of an induction motor (IM). This method is carried out in the IM standstill configuration and uses adaptive linear neuron (ADALINE) networks. In order to simplify the identification process, the IM model is approximated by two first-order subsystems: one is valid at low frequencies (LFs), called the slow system and the other is valid at high frequencies (HFs), called the fast system. By means of two ADALINE networks, the parameters of the slow system and the fast system are identified in LFs and HFs, respectively, and thus, the required IM parameters are derived. Finally, experimental results are presented in order to validate the proposed method and to check the accuracy of the obtained parameters. The originality of this paper is the building of a model representation that is suitable for implementation with ADALINE networks. This leads to a simple implementation and ease of parameters identification. [ABSTRACT FROM AUTHOR]

Abstract: The paper is concerned with the uncertain parameters and time-delays of chaos system with random noises. A scheme based on differential evolution algorithm (DE) is newly introduced to solve the problem via a nonnegative multi-modal nonlinear optimization, which finds a best combination of parameters and time-delays such that an objective function is minimized. The illustrative examples, in both systems free of time-delays and time-delays systems with random noises, are given to demonstrate the validity of the proposed method. [Copyright &y& Elsevier]

Abstract: The constitutive relations of the mass loaded paper honeycomb panel is expressed as a linear differential equation. Using the Laplace transform method, we can model the paper honeycomb panel as a linear material with viscoelastic property, the relaxation kernel is expressed as the sum of complex exponentials. A parameters identification method is formulated using the substitute method. A free response experiment system is set up, the free response data are recorded and used to identify the stiffness and damping coefficients as well as the viscoelastic parameters. The identified parameters of the model under different honeycomb core height condition are given in this paper. The model and the identified parameters can be used to simulate the vibration transmissibility of the mass loaded paper honeycomb panel. The response of the system excited by the harmonic inputs is calculated using the one-term harmonic balance method. The comparison of the simulated transmissibility curve and the experimental data indicates that the model presented in this paper is accurate, the parameters presented can be used to predict the vibration transmissibility property of the mass loaded paper honeycomb panel system. [Copyright &y& Elsevier]

The purpose of this study was to effectively identify parameters for a LuGre friction model based on experimental measures. In earlier work related to this study (Yang et al., 2009), which was based on the characters of polygonal wear (Sueoka and Ryu, 1997), we showed a frictional vibration model for a mass on a moving belt. This model reflected lateral vibrations caused by velocity and toe-in angle. An important aspect of the present study is the improved friction model. A previous friction model, which divided the process into four parts, expressed the sable excited vibration well but failed to reflect the hysteresis loop change when vehicles accelerated or decelerated continuously. A LuGre friction model can solve this problem, but several model parameters must be obtained experimentally. We measured contact width and length of tires as vertical pressure changed; this provided a theoretical basis for apparent stiffness of a unit of tire tread. Based on tire data from Bakker E's article in a SAE paper from 1987, we identified the Stribeck exponent and Stribeck velocity in LuGre. Then, the results were implemented in a vibration system that verified the rationality of the data. [ABSTRACT FROM AUTHOR]

Abstract: Synchronous generator (SG) modeling plays an important role in system planning, operation and post-disturbance analysis. This paper presents an improved algorithm named Particle Swarm Optimization with Quantum Operation (PSO–QO) to solve both offline and online parameters estimation problem for SG. First, the hybrid algorithm is proposed to increase the convergence speed and identification accuracy of the basic Particle Swarm Optimization (PSO). An illustrative example for parameters identification of SG is provided to confirm the validity, as compared with Linearly Decreasing Inertia Weight PSO (LDW-PSO), and the Quantum Particle Swarm Optimization (QPSO) in terms of parameter estimation accuracy and convergence speed. Second, PSO–QO is also improved to detect and determine parameters variation. In this case, a sentry particle is introduced to detect any changes in system parameters. Simulation results confirm that the proposed algorithm is a viable alternative for online parameters detection and parameters identification of SG. [Copyright &y& Elsevier]

Abstract: This paper deals with a numerical NDT method to identify mechanical parameters by inverse analysis using the particle swarm optimization algorithm. This method is using a finite element model of the structure to study in order to minimize the error between field data and data predicted by the FE model. The method is applied in the case of a laboratory experiment modelling a soil–structure interaction problem. Results stress the impact of the sensitivity of the solution to the accuracy of field measurements by simulating both different levels of measurement noise on data and using several placements of sensors. [Copyright &y& Elsevier]

In this article, we describe an approach to model the electromechanical behavior of the skeletal muscle based on the Huxley formulation. We propose a model that complies with a well established macroscopic behavior of striated muscles where force-length, force-velocity, and Mirsky-Parmley properties are taken into account. These properties are introduced at the microscopic scale and related to a tentative explanation of the phenomena. The method used integrates behavior ranging from the microscopic to the macroscopic scale, and allows the computation of the dynamics of the output force and stiffness controlled by EMG or stimulation parameters. The model can thus be used to simulate and carry out research to develop control strategies using electrical stimulation in the context of rehabilitation. Finally, through animal experiments, we estimated model parameters using a Sigma Point Kalman Filtering technique and dedicated experimental protocols in isometric conditions and demonstrated that the model can accurately simulate individual variations and thus take into account subject dependent behavior. [ABSTRACT FROM AUTHOR]

Abstract: In the present paper, the nonlinear Internal Model Control (IMC) method is introduced and applied to linearize high frequency power amplifiers (PAs). The IMC is based on the description of a process model and of a controller. It is shown that baseband frequency descriptions are suitable for the model and the controller. Their description parameters are derived from input and output modulation signals processed in Cartesian form. Simulation results are given to illustrate the design procedure and to demonstrate the performances of the IMC linearizer. [Copyright &y& Elsevier]

Systems biology aims at building computational models of biological pathways in order to study in silico their behaviour and to verify biological hypotheses. Modelling can become a new powerful method in molecular biology, if correctly used. Here we present step-by-step the derivation and identification of the dynamical model of a biological pathway using a novel synthetic network recently constructed in the yeast Saccharomyces cerevisiae for In-vivo Reverse-Engineering and Modelling Assessment. This network consists of five genes regulating each other transcription. Moreover, it includes one protein-protein interaction, and its genes can be switched on by addition of galactose to the medium. In order to describe the network dynamics, we adopted a deterministic modelling approach based on non-linear differential equations. We show how, through iteration between experiments and modelling, it is possible to derive a semi-quantitative prediction of network behaviour and to better understand the biology of the pathway of interest. [ABSTRACT FROM AUTHOR]

Abstract: This paper is devoted to the modelling of a specific ramming mix mainly used in the high-temperature industry due to its high-compacting behaviour. This material has the ability to absorb the deformation of parts submitted to high thermal loads. Triaxial and instrumented die compaction tests were carried out in order to identify the shear and hardening behaviours, respectively. Tests on the ramming mix were led for a temperature range between 20°C and 80°C. The temperature effect is particularly observed on the material response when it is compacted. The main features of the behaviour of the ramming mix can be represented by the theoretical framework of the Modified Cam-Clay model. A single variable allows to accurately reproduce the hardening behaviour depending on the temperature. Moreover, an extension of the model for the hardening behaviour at high pressures is proposed. A good agreement between the experimental data and numerical tests is reached with this model. [ABSTRACT FROM AUTHOR]

novel maglev transportation system was proposed for large travel range ultra precision motion. The system consists of a levitation subsystem and a propulsion subsystem. During the propulsion subsystem driving the moving platform along the guideway, the levitation subsystem uses six pairs of electromagnets to steadily suspend the moving platform over the guideway. The model of the levitation system, which is a typical nonlinear multi-input multi-output coupling system and has many inner nonlinear coupling characteristics, was deduced. For testifying the model, the levitation mechanism was firstly controlled by proportional-integral-differential (PID) control, and then a lot of input-output data were collected for model parameter identification. The least-square parameter identification method was used. The identification results prove that the model is feasible and suitable for the real system. [ABSTRACT FROM AUTHOR]

This paper investigates projectively lag synchronization of a new hyperchaotic system (proposed by Yang et al. in Nonlinear Anal., Real World Appl., 10:1601, ), with certain/uncertain parameters. Based on the Lyapunov function constructing method and application of the Babarǎt lemma, projectively lag synchronization is achieved for the new hyperchaotic system by designing nonlinear controllers. Furthermore, a novel method is proposed to identify unknown parameters based on projectively lag synchronization of the new hyperchaotic system. Finally, several numerical simulations are given to verify and test the correctness and effectiveness of the novel methods we proposed. [ABSTRACT FROM AUTHOR]

The modern systems biology approach to the study of molecular cellular biology, consists in the development of computational tools to support the formulation of new hypotheses on the molecular mechanisms underlying the observed cell behavior. Recent biotechnologies are able to provide precise measures of gene expression time courses in response to a large variety of internal and environmental perturbations. In this paper, we propose a simple algorithm for the selection of the “best” regulatory network motif among a number of alternatives, using the expression time course of the genes which are the final targets of the activated signalling pathway. To this aim, we considered the Hill nonlinear ODEs model to simulate the behavior of two ubiquitous motifs: the single input motif and the multi output feed-forward loop motif. Our algorithm has been tested on simulated noisy data assuming the presence of a step-wise regulatory signal. The results clearly show that our method is potentially able to robustly discriminate between alternative motifs, thus providing a useful in silico identification tool for the experimenter. [ABSTRACT FROM AUTHOR]

Abstract: This paper focuses on the identification of nonlinear dynamic models for physical systems such as electrostatically actuated micro-electro-mechanical systems (MEMS). The proposed approach consists in transforming, by means of suitable global operations, the input–output differential model in such a way that the new equivalent formulation is well adapted to the identification problem, thanks to the following properties: first, the linearity with respect to the parameters to be identified is preserved, second, the continuous dependence on noise measurements is restored. Consequently, a simple least-square resolution can be used, in such a way that some of the difficulties classically encountered with identification methods are by-passed. The method is implemented on real measurement data from a physical system. [Copyright &y& Elsevier]

Speed estimation method for sensorless control of induction machine drive has gained increasing interest among the research community. The supremacy of an induction machine drive depends on the speed estimation accuracy. To ensure accurate speed estimation over a wide range, from zero to high levels exceeding the rated speed, accurate values of the machine's parameters should be given online. Neural Fuzzy and Fuzzy-neural systems were developed for estimating stator inductance, magnetic dispersion coefficient and rotor time constant for induction machine drive sensorless control. The performance and limitations of each system have been verified by simulation results over a wide speed range up to values significantly exceeding the rated speed. [ABSTRACT FROM AUTHOR]

Abstract: This paper considers time-dependent identification problems governed by some generalized transient bioheat transfer type models in biological systems with porous structures and directional blood flow. The analysis and the real applications of the porous media models and biofluid heat transfer in living tissues are relatively recent, however the blood perfusion rate and the porosity parameter affect considerably the effects of thermal physical properties on the transient temperature of biological tissues. The control considered in this work estimates simultaneously these two parameters. The result can be very beneficial for thermal diagnostics in medical practices, for example for laser surgery, photo and thermotherapy for regional hyperthermia, often used in the treatment of cancer. First, the mathematical models are introduced and the existence, the uniqueness and the regularity of the solution of the state equation are proved as well as stability and maximum principle under extra assumptions. Afterwards the identification problems with Tichonov regularization are formulated, in different situations, in order to control the online temperature given by radiometric measurement. An optimal solution is proven to exist and finally necessary optimality conditions are given. Some strategies for numerical realization based on the adjoint variables are provided. [Copyright &y& Elsevier]