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Conventional design and optimization of passive compensation network (PCN) for inductive power transfer (IPT) system are based on specific topologies. The demerits of this design method are: i) The topology is mostly chosen by experience; ii) The design parameters are not multi-objective optimal. Aiming at these issues, this paper proposes an optimal PCN design scheme based on evolutionary algorithm (EA) to synchronously optimize the topology and parameters of PCN for IPT system. Firstly, a unified mathematical model of the PCN is presented and derived by transmission matrix. Then, according to the mathematical model, the multi-objective functions (such as output fluctuation and efficiency) as well as the constraints (such as load and coupling coefficient) for the optimal PCN design are established. The EA based multi-objective optimal PCN design algorithm is further constructed. Six optimal results are obtained using the algorithm, and one optimized PCN having minimum output current fluctuation and high-efficiency is chosen to validate the effectiveness of the proposed design scheme in experiment. For the given IPT system with the optimized PCN, the maximum fluctuation of output current is no more than 11%, within 200% of load variation and about 77% of coupling variation. [ABSTRACT FROM AUTHOR]

We study in this paper, a class of constrained linear-quadratic (LQ) optimal control problem formulations for the scalar-state stochastic system with multiplicative noise, which has various applications, especially in the financial risk management. The linear constraint on both the control and state variables considered in our model destroys the elegant structure of the conventional LQ formulation and has blocked the derivation of an explicit control policy so far in the literature. We successfully derive in this paper, the analytical control policy for such a class of problems by utilizing the state separation property induced from its structure. We reveal that the optimal control policy is a piecewise affine function of the state and can be computed offline efficiently by solving two coupled Riccati equations. Under some mild conditions, we also obtain the stationary control policy for an infinite time horizon. We demonstrate the implementation of our method via some illustrative examples and show how to calibrate our model to solve dynamic constrained portfolio optimization problems. [ABSTRACT FROM AUTHOR]

Recently, there has been significant interest in convex relaxations of the optimal power flow (OPF) problem. A semidefinite programming (SDP) relaxation globally solves many OPF problems. However, there exist practical problems for which the SDP relaxation fails to yield the global solution. Conditions for the success or failure of the SDP relaxation are valuable for determining whether the relaxation is appropriate for a given OPF problem. To move beyond existing conditions, which only apply to a limited class of problems, a typical conjecture is that failure of the SDP relaxation can be related to physical characteristics of the system. By presenting an example OPF problem with two equivalent formulations, this paper demonstrates that physically based conditions cannot universally explain algorithm behavior. The SDP relaxation fails for one formulation but succeeds in finding the global solution to the other formulation. Since these formulations represent the same system, success (or otherwise) of the SDP relaxation must involve factors beyond just the network physics. The lack of universal physical conditions for success of the SDP relaxation motivates the development of tighter convex relaxations capable of solving a broader class of problems. Tools from polynomial optimization theory provide a means of developing tighter relaxations. This paper uses the example problem to illustrate relaxations from the Lasserre hierarchy for polynomial optimization and a related “mixed semidefinite/second-order cone programming” hierarchy. [ABSTRACT FROM AUTHOR]

This paper develops and validates a comprehensive and universally applicable computational concept for solving nonlinear differential equations (NDEs) through a neurocomputing concept based on cellular neural networks (CNNs). High-precision, stability, convergence, and lowest-possible memory requirements are ensured by the CNN processor architecture. A significant challenge solved in this paper is that all these cited computing features are ensured in all system-states (regular or chaotic ones) and in all bifurcation conditions that may be experienced by NDEs.One particular quintessence of this paper is to develop and demonstrate a solver concept that shows and ensures that CNN processors (realized either in hardware or in software) are universal solvers of NDE models. The solving logic or algorithm of given NDEs (possible examples are: Duffing, Mathieu, Van der Pol, Jerk, Chua, Rössler, Lorenz, Burgers, and the transport equations) through a CNN processor system is provided by a set of templates that are computed by our comprehensive templates calculation technique that we call nonlinear adaptive optimization. This paper is therefore a significant contribution and represents a cutting-edge real-time computational engineering approach, especially while considering the various scientific and engineering applications of this ultrafast, energy-and-memory-efficient, and high-precise NDE solver concept. For illustration purposes, three NDE models are demonstratively solved, and related CNN templates are derived and used: the periodically excited Duffing equation, the Mathieu equation, and the transport equation. [ABSTRACT FROM PUBLISHER]

Checking validity of a model is a crucial step in the process of system identification. This is especially true when dealing with switched affine systems since, in this case, the problem of system identification from noisy data is known to be generically NP-Hard and can only be solved in practice by using heuristics and relaxations. Therefore, before the identified models can be used for instance for controller design, they should be systematically validated against additional experimental data. In this paper we address the problem of model (in)validation for multi-input multi-output switched affine systems in output error form with unknown switches. As a first step, we prove that necessary and sufficient invalidation certificates can be obtained by solving a sequence of convex optimization problems. In principle, these problems involve increasingly large matrices. However, as we show in the paper by exploiting recent results from semialgebraic geometry, the proposed algorithm is guaranteed to stop after a finite number of steps that can be be explicitly computed from the a priori information. In addition, this algorithm exploits the sparse structure of the underlying optimization problem to substantially reduce the computational burden. The effectiveness of the proposed method is illustrated using both academic examples and a non-trivial problem arising in computer vision: activity monitoring. [ABSTRACT FROM AUTHOR]

In this paper, we present a framework for analytically estimating the output quality of common digital signal processing (DSP) blocks that utilize approximate adders. The framework is based on considering the error of approximate adders as an additive noise (approximation noise) that disturbs the output of the DSP block in question. A signal processing theoretical modeling approach for describing the power of the approximation noise which is the integral of error spectral density over the bandwidth, is developed. The output qualities of DSP blocks, such as finite impulse response filter, discrete cosine transform, and fast Fourier transform, which utilize approximate adders, are thus estimated. The accuracy of the proposed framework is evaluated by comparing mathematical model predictions to simulation results by using the signal-to-noise ratio (SNR) metric. The inaccuracy of the SNRs predicted by the framework was, on average, less than 2.5dB compared with that obtained from simulations. Therefore, a mathematical optimization approach based on Lagrange Multipliers for optimizing design parameters is also presented. The optimization is realized by choosing a proper configuration of the target block, such as determining the data width of the inexact computation part for each approximate adder in the design. [ABSTRACT FROM AUTHOR]

Due to the emerging systems with constraints in terms of power and costs, such as smart sensor interfaces for the Internet-of-Things, the design of the ADCs becomes very challenging. In this paper, energy and area efficient techniques for continuous-time (CT) delta-sigma modulators ($\Delta \Sigma $ Ms) are discussed. These techniques are based on increasing the contribution of the 1-bit comparator to the loop gain by using passive RC integrators together with low gain amplifiers in the $\Delta \Sigma \text{M}$ loop filter. A third-order CT $\Delta \Sigma \text{M}$ is designed using these techniques to demonstrate their validity, and it achieves 27.5 fJ/conv.-step of energy efficiency. Due to the many design issues, such as the tradeoff between RC variations and loop stability, the design of this modulator has been optimized using a genetic algorithm. The 65-nm CMOS $\Delta \Sigma \text{M}$ occupies only $0.013~\mathrm {mm^{2}}$ , dissipates 256 $\mu \text{W}$ from a 0.7-V supply and it achieves a peak SNDR of 69.1 dB in a 2-MHz bandwidth. The dynamic range reaches 76.2 dB, which corresponds to a $\mathrm {FoM_{Schreier}}$ of 175.1 dB. [ABSTRACT FROM AUTHOR]

This paper presents comprehensive analyses of power-performance tradeoffs in current-mode logic (CML)-based transmitters via equation-based optimization and provides practical design guidelines that can help achieving optimum power efficiency. An accurate equation-based optimization framework was developed for this purpose, and using this framework, key circuit-level design parameters were found with specific design specifications and process technologies. The proposed optimization framework can determine optimum clock edge-rate (i.e., ratio of rise/fall time to clock period) and inter-stage voltage swings that enable significant power savings without affecting performance. Various transmitter design tradeoffs were analyzed for 45-nm, 65-nm, and 90-nm technologies considering data rate, clock edge-rate, and inter-stage swing optimization. Based on the analysis results, we determined the most energy-efficient data rate that can be achieved with acceptable power overhead for each technology node. Also, we identified the optimal clock edge-rate and inter-stage voltage swings in CML gates that achieved minimum transmitter power dissipation. Our analysis results indicate that, when properly optimized, overall transmitter power consumption can be reduced by up to 43% with inter-stage swing optimization with all other design constraints held constant. [ABSTRACT FROM AUTHOR]

Comparator power consumption is a major bottleneck to the power efficiency of a high resolution successive approximation register (SAR) analog-to-digital converter (ADC) used in low-power applications. This paper analyzes theoretically the optimal comparators that need to be used to achieve a desired overall performance at minimum power levels. A simple and accurate mathematical model of the SAR ADC that is amenable to analysis is first presented. The mathematical formulation suggests that the comparison power allocated to each bit step need not be the same. The optimization problem, therefore, is solved so that the comparator power budget is optimally distributed among all bit steps. Simulation results show that up to 50% and 60% power savings can be achieved when 10 and 12 comparators are employed for 10 b and 12 b SAR ADCs, respectively. To reduce the implementation complexity, comparator noise allocation problem is also solved when fewer than N comparators are employed in an N-bit SAR ADC. Simulation results suggest that two comparators is sufficient to achieve near ideal performance in 10 b and 12 b SAR ADCs. [ABSTRACT FROM AUTHOR]

This paper addresses a general method for parameter identification in distributed parameter systems (DPSs) based on a reference model and measurement information. Moreover, the developed identification method is used to deal with secure communication problems. Firstly, in combination with the historical information measured by sensors, an iterative approach based on an approximator is put forward. Meanwhile, an execution scheme for the iterative approach is provided by an optimization policy. Subsequently, by bringing new mechanisms into quantum particle swarm optimization (QPSO) algorithm to enhance its search capability, we propose quantum-leading-following-based optimization (QLFBO) algorithm, which serves the optimization policy. Further, the global convergence for QLFBO algorithm is proved combined with the parameter identification problem of DPSs, and the computational complexity on this algorithm is discussed in detail. Finally, simulation experiments on the diffusion-reaction model are carried out by the iterative approach based on QLFBO algorithm under different measurement ways. Simultaneously, secure communication depending on this model is performed in the double-scroll Chua’s circuit and a lotus image via the investigated iterative strategy and encryption-decryption scheme. Simulation results verify the validity and practicability of the developed method. [ABSTRACT FROM AUTHOR]

This paper proposes scalable, distributed algorithms for solving linear equations by integrating two mechanisms, termed consensus and conservation, in double-layered multiagent networks. The multiagent network considered in this paper is composed of clusters and each cluster consists of an aggregator and a subnetwork of agents. By achieving consensus and conservation through agent–agent communications in the same cluster and aggregator–aggregator communications among different clusters, respectively, distributed algorithms are devised for agents to cooperatively achieve a solution to the overall linear equation. These algorithms outperform existing algorithms, including but not limited to the following aspects—first, each agent does not have to know as much as a complete row or column of the overall equation; second, each agent only needs to control as few as two scalar states when the number of clusters and the number of agents are sufficiently large; third, the dimensions of agents’ states in the proposed algorithms do not have to be the same (while in contrast, algorithms based on the idea of standard consensus inherently require all agents’ states to be of the same dimension). Both analytical proof and simulation results are provided to validate exponential convergence of the proposed distributed algorithms in solving linear equations. [ABSTRACT FROM AUTHOR]

This paper presents a two-time-scale neurodynamic approach to constrained minimax optimization using two coupled neural networks. One of the recurrent neural networks is used for minimizing the objective function and another is used for maximization. It is shown that the coupled neurodynamic systems operating in two different time scales work well for minimax optimization. The effectiveness and characteristics of the proposed approach are illustrated using several examples. Furthermore, the proposed approach is applied for $H_\infty $ model predictive control. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a neural network modeled by a differential inclusion to solve a class of nonsmooth convex optimization problems, where the constraints are defined by a class of nonsmooth convex inequality constraints and a class of affine equality constraints. For any initial point, the solution of the proposed network is global existent, unique and uniformly bounded, which is just the “slow solution” of network. By the regularization item, without any estimation on the exact penalty parameters, the solution of proposed network is convergent to the optimal solution set of optimization problem. Moreover, when the feasible region satisfies another condition, the solution of proposed network converges to the feasible region in finite time and to the particular element in the optimal solution set with the smallest norm, which indicates that our proposed neural network is globally attractive. Some numerical examples are presented to illustrate the effectiveness of the proposed neural network for solving nonsmooth convex optimization problems. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the fuzzy constrained matrix game (MG) problems using the concepts of recurrent neural networks (RNNs). To the best of our knowledge, this paper is the first in attempting to find a solution for fuzzy game problems using RNN models. For this purpose, a fuzzy game problem is reformulated into a weighting problem. Then, the Karush–Kuhn–Tucker (KKT) optimality conditions are provided for the weighting problem. The KKT conditions are used to propose the RNN model. Moreover, the Lyapunov stability and the global convergence of the RNN model are also confirmed. Finally, three illustrative examples are presented to demonstrate the effectiveness of this approach. The obtained results are compared with the results obtained by the previous approaches for solving fuzzy constrained MG. [ABSTRACT FROM AUTHOR]

Partial least squares regression (PLSR) has been a popular technique to explore the linear relationship between two data sets. However, all existing approaches often optimize a PLSR model in Euclidean space and take a successive strategy to calculate all the factors one by one for keeping the mutually orthogonal PLSR factors. Thus, a suboptimal solution is often generated. To overcome the shortcoming, this paper takes statistically inspired modification of PLSR (SIMPLSR) as a representative of PLSR, proposes a novel approach to transform SIMPLSR into optimization problems on Riemannian manifolds, and develops corresponding optimization algorithms. These algorithms can calculate all the PLSR factors simultaneously to avoid any suboptimal solutions. Moreover, we propose sparse SIMPLSR on Riemannian manifolds, which is simple and intuitive. A number of experiments on classification problems have demonstrated that the proposed models and algorithms can get lower classification error rates compared with other linear regression methods in Euclidean space. We have made the experimental code public at https://github.com/Haoran2014. [ABSTRACT FROM AUTHOR]

This paper is concerned with variational optimal control constructions whose solution yields a sampling algorithm. The particular form of the sampling algorithm considered here is a particle filter, designed to numerically approximate the solution to the global optimization problem. The theoretical significance of this study comes from its variational aspects. Specifically, the control input represents the solution of a mean-field-type optimal control problem. Its parametric counterpart, obtained when a parametric form of density is known a priori, is shown to be equivalent to the natural gradient algorithm. Explicit formulae for the filter are derived when the objective function is quadratic and the density is Gaussian. The optimal control construction of the particle filter is a significant departure from the classical importance sampling–resampling-based approaches. [ABSTRACT FROM AUTHOR]

In this paper, a learning-based predictive control (LPC) scheme is proposed for adaptive optimal control of discrete-time nonlinear systems under stochastic disturbances. The proposed LPC scheme is different from conventional model predictive control (MPC), which uses open-loop optimization or simplified closed-loop optimal control techniques in each horizon. In LPC, the control task in each horizon is formulated as a closed-loop nonlinear optimal control problem and a finite-horizon iterative reinforcement learning (RL) algorithm is developed to obtain the closed-loop optimal/suboptimal solutions. Therefore, in LPC, RL and adaptive dynamic programming (ADP) are used as a new class of closed-loop learning-based optimization techniques for nonlinear predictive control with stochastic disturbances. Moreover, LPC also decomposes the infinite-horizon optimal control problem in previous RL and ADP methods into a series of finite horizon problems, so that the computational costs are reduced and the learning efficiency can be improved. Convergence of the finite-horizon iterative RL algorithm in each prediction horizon and the Lyapunov stability of the closed-loop control system are proved. Moreover, by using successive policy updates between adjoint time horizons, LPC also has lower computational costs than conventional MPC which has independent optimization procedures between two different prediction horizons. Simulation results illustrate that compared with conventional nonlinear MPC as well as ADP, the proposed LPC scheme can obtain a better performance both in terms of policy optimality and computational efficiency. [ABSTRACT FROM AUTHOR]

The problem of low-rank approximation with convex constraints, which appears in data analysis, system identification, model order reduction, low-order controller design, and low-complexity modeling is considered. Given a matrix, the objective is to find a low-rank approximation that meets rank and convex constraints while minimizing the distance to the matrix in the squared Frobenius norm. In many situations, this nonconvex problem is convexified by nuclear-norm regularization. However, we will see that the approximations obtained by this method may be far from optimal. In this paper, we propose an alternative convex relaxation that uses the convex envelope of the squared Frobenius norm and the rank constraint. With this approach, easily verifiable conditions are obtained under which the solutions to the convex relaxation and the original nonconvex problem coincide. A semidefinite programming representation of the convex envelope is derived, which allows us to apply this approach to several known problems. Our example on optimal low-rank Hankel approximation/model reduction illustrates that the proposed convex relaxation performs consistently better than nuclear-norm regularization and may outperform balanced truncation. [ABSTRACT FROM AUTHOR]

This paper presents a projection neural network described by a dynamic system for solving constrained quadratic minimax programming problems. Sufficient conditions based on a linear matrix inequality are provided for global convergence of the proposed neural network. Compared with some of the existing neural networks for quadratic minimax optimization, the proposed neural network in this paper is capable of solving more general constrained quadratic minimax optimization problems, and the designed neural network does not include any parameter. Moreover, the neural network has lower model complexities, the number of state variables of which is equal to that of the dimension of the optimization problems. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network. [ABSTRACT FROM PUBLISHER]

Several convex relaxations of the optimal power flow (OPF) problem have recently been developed using both bus injection models and branch flow models. In this paper, we prove relations among three convex relaxations: a semidefinite relaxation that computes a full matrix, a chordal relaxation based on a chordal extension of the network graph, and a second-order cone relaxation that computes the smallest partial matrix. We prove a bijection between the feasible sets of the OPF in the bus injection model and the branch flow model, establishing the equivalence of these two models and their second-order cone relaxations. Our results imply that, for radial networks, all these relaxations are equivalent and one should always solve the second-order cone relaxation. For mesh networks, the semidefinite relaxation and the chordal relaxation are equally tight and both are strictly tighter than the second-order cone relaxation. Therefore, for mesh networks, one should either solve the chordal relaxation or the SOCP relaxation, trading off tightness and the required computational effort. Simulations are used to illustrate these results. [ABSTRACT FROM PUBLISHER]

Periodic optimal control problems (POCPs) based on dynamic models holding invariants are often problematic to treat using standard numerical methods. The difficulty stems from a failure of standard constraint qualifications and typically hinders the convergence of the numerical solver, or even defeats it. Optimization problems having weak constraint qualifications can be treated using dedicated solvers, at the price of a more involved algorithmic. In this paper, we analyze the constraint qualification of POCPs holding invariants, and propose three simple and computationally inexpensive modifications of the formulation that allow for a recovery of linear independence constraint qualification, while not affecting the second-order sufficient conditions for optimality. Hence, the resulting POCP can be tackled via standard solvers, without special treatment. The application of these approaches is detailed for the case of POCPs holding index-reduced differential-algebraic equations and representations of the $\mathrm{SO}\left(3\right)$ Lie group. [ABSTRACT FROM AUTHOR]

Domain adaptation learning is one of the fundamental research topics in pattern recognition and machine learning. This paper introduces a regularized semipaired kernel canonical correlation analysis formulation for learning a latent space for the domain adaptation problem. The optimization problem is formulated in the primal-dual least squares support vector machine setting where side information can be readily incorporated through regularization terms. The proposed model learns a joint representation of the data set across different domains by solving a generalized eigenvalue problem or linear system of equations in the dual. The approach is naturally equipped with out-of-sample extension property, which plays an important role for model selection. Furthermore, the Nyström approximation technique is used to make the computational issues due to the large size of the matrices involved in the eigendecomposition feasible. The learned latent space of the source domain is fed to a multiclass semisupervised kernel spectral clustering model that can learn from both labeled and unlabeled data points of the source domain in order to classify the data instances of the target domain. Experimental results are given to illustrate the effectiveness of the proposed approaches on synthetic and real-life data sets. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel recurrent neural network to resolve the redundancy of manipulators for efficient kinematic control in the presence of noises in a polynomial type. Leveraging the high-order derivative properties of polynomial noises, a deliberately devised neural network is proposed to eliminate the impact of noises and recover the accurate tracking of desired trajectories in workspace. Rigorous analysis shows that the proposed neural law stabilizes the system dynamics and the position tracking error converges to zero in the presence of noises. Extensive simulations verify the theoretical results. Numerical comparisons show that existing dual neural solutions lose stability when exposed to large constant noises or time-varying noises. In contrast, the proposed approach works well and has a low tracking error comparable to noise-free situations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a robust regulator for discrete-time Markovian jump linear systems (DMJLS) subject to structured parameter uncertainties. States of Markov chain are considered known at each instant and the transition probabilities matrix is time-varying. The framework proposed to deal with this problem is developed through a regularization approach based on robust least-squares and penalty functions. Recursive solutions in terms of coupled Riccati equations, which do not depend on any auxiliary parameter to be tuned, are provided. These equations resemble the recursive solutions of DMJLS standard regulators when the system is not subject to uncertainties. Conditions for convergence and stability of this robust regulator are established. Numerical examples present comparative studies considering the proposed robust regulator, the standard linear quadratic regulator, and an H\infty -controller for DMJLS. [ABSTRACT FROM PUBLISHER]

This paper presents a novel neural network for solving generally constrained variational inequality problems by constructing a system of double projection equations. By defining proper convex energy functions, the proposed neural network is proved to be stable in the sense of Lyapunov and converges to an exact solution of the original problem for any starting point under the weaker cocoercivity condition or the monotonicity condition of the gradient mapping on the linear equation set. Furthermore, two sufficient conditions are provided to ensure the stability of the proposed neural network for a special case. The proposed model overcomes some shortcomings of existing continuous-time neural networks for constrained variational inequality, and its stability only requires some monotonicity conditions of the underlying mapping and the concavity of nonlinear inequality constraints on the equation set. The validity and transient behavior of the proposed neural network are demonstrated by some simulation results. [ABSTRACT FROM PUBLISHER]

Feasibility determination has emerged as a widely applied problem in simulation optimization. It seeks to provide all the feasible designs from a finite set of design alternatives based on which the final decision can be chosen by the decision maker. In this paper, we consider the feasibility determination problem in presence of multiple performance measure constraints. The optimal solution to maximize the probability of correct feasibility determination is derived under asymptotic approximation. A corresponding sequential selection procedure is designed for implementation. The numerical testing shows that our approach can enhance the simulation efficiency significantly. [ABSTRACT FROM PUBLISHER]

This paper considers decentralized control and optimization methodologies for large populations of systems, consisting of several agents with different individual behaviors, constraints and interests, and influenced by the aggregate behavior of the overall population. For such large-scale systems, the theory of aggregative and mean field games has been established and successfully applied in various scientific disciplines. While the existing literature addresses the case of unconstrained agents, we formulate deterministic mean field control problems in the presence of heterogeneous convex constraints for the individual agents, for instance arising from agents with linear dynamics subject to convex state and control constraints. We propose several model-free feedback iterations to compute in a decentralized fashion a mean field Nash equilibrium in the limit of infinite population size. We apply our methods to the constrained linear quadratic deterministic mean field control problem. [ABSTRACT FROM PUBLISHER]

This paper proposes a new iteration-free steady-state analysis to the Class-E power amplifier (PA) in frequency-domain by extending the scope of electrical impedance. Owning to this extension, the impedances of different circuit components in the Class-E PA circuits, including the active switches, can be expressed in matrix form. Based on the conventional circuit laws, e.g., the series and parallel laws and the Ohm's law, the steady-state characteristics of a whole Class-E PA circuit can be obtained by vector and matrix manipulation. The number of harmonics involved in the calculation have effects on the computational efficiency and accuracy. The influences of some circuit conditions towards the selection of harmonic number are discussed. The proposed formulation enables fast mapping of some performance indices, e.g., the output power and conversion efficiency, under steady state, so that the changing trends of these indices with respect to the variations of specified circuit parameters can be estimated. This feature can be utilized to carry out offline optimization by tuning some passive components, or online optimization by tuning two in situ adjustable parameters, i.e., the switch driving frequency and duty cycle. The later scheme provides preliminary knowledge on the in situ tuning of Class-E PA in the load varying applications, e.g., electric process heating and wireless power link. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a general noise optimization technique for the CMOS common-source low-noise amplifiers. By directly employing the short-channel MOSFET I–V characteristic and van der Ziel's noise model, we derive design equations for the selection of the circuit design parameters, such as transistor sizes, passive component values, and bias voltages, which are subject to various gain and current consumption constraints. We also include several side effects, including the finite quality factor and the back-gate transconductance into the optimization process, and analyze their impact on the optimization results. Design examples that are based on a virtual but realistic process are given to verify our analysis and to give design intuition. [ABSTRACT FROM PUBLISHER]

MOS current-mode logic (MCML) is a low-noise alternative to CMOS logic. The lack of MCML automation tools, however, has deterred designers from applying MCML to complex digital functions. This paper presents an efficient MCML optimization program that can be used to properly size MCML gates. The delay model accuracy is adjusted by fitting measured gate delays by means of technology-dependent parameters. For an N number of logic gates, the proposed mathematical program has reduced the number of variables to N+1, in comparison to 7N+1 in the most recent work on this topic. The program has been implemented to efficiently optimize a 4-bit ripple carry adder and an 8-bit decoder in 0.18-\mu\ m CMOS technology. [ABSTRACT FROM PUBLISHER]

Energy optimization for plug-in hybrid electric vehicles (PHEVs) is a challenging problem due to the system complexity and many physical and operational constraints in PHEVs. In this paper, we present a Q-learning-based in-vehicle learning system that is free of physical models and can robustly converge to an optimal energy control solution. The proposed machine learning algorithms combine neuro-dynamic programming (NDP) with future trip information to effectively estimate the expected future energy cost (expected cost-to-go) for a given vehicle state and control actions. The convergences of these learning algorithms were demonstrated on both fixed and randomly selected drive cycles. Based on the characteristics of these learning algorithms, we propose a two-stage deployment solution for PHEV power management applications. Furthermore, we introduce a new initialization strategy, which combines the optimal learning with a properly selected penalty function. This initialization scheme can reduce the learning convergence time by 70%, which is a significant improvement for in-vehicle implementation efficiency. Finally, we develop a neural network (NN) for predicting battery state-of-charge (SoC), rendering the proposed power management controller completely free of physical models. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to compute the distance to instability for linear time-invariant delay systems with multiple delays, subjected to combined uncertainties on the system matrices and the delay parameters. The method is able to fully exploit structure and possible interdependencies between the uncertainties affecting the coefficient matrices, the property that realistic perturbations are real valued, and the structure of the delay equation. The resulting algorithm relies on a characterization in terms of the pseudospectral abscissa and on techniques from optimization over manifolds of matrices. [ABSTRACT FROM AUTHOR]

In this paper, a bi-projection neural network for solving a class of constrained quadratic optimization problems is proposed. It is proved that the proposed neural network is globally stable in the sense of Lyapunov, and the output trajectory of the proposed neural network will converge globally to an optimal solution. Compared with existing projection neural networks (PNNs), the proposed neural network has a very small model size owing to its bi-projection structure. Furthermore, an application to data fusion shows that the proposed neural network is very effective. Numerical results demonstrate that the proposed neural network is much faster than the existing PNNs. [ABSTRACT FROM AUTHOR]

In this paper, an improved method to construct and estimate Volterra models using Generalized Orthonormal Basis Functions (GOBF) is presented. The proposed method extends results obtained in previous works, where an exact technique for optimizing the GOBF parameters (poles) for symmetric Volterra models of any order was presented. The proposed extensions take place in two different ways: (i) the new formulation is derived in such a way that each multidimensional kernel of the model is decomposed into a set of independent orthonormal bases (rather than a single, common basis), each of which is parameterized by an individual set of poles responsible for representing the dominant dynamic of the kernel along a particular dimension; and (ii) the new formulation is based on a ladder-structured GOBF architecture that is characterized by having only real-valued parameters to be estimated, regardless of whether the GOBF poles encoded by these parameters are real- or complex-valued. The exact gradients of an error functional with respect to the parameters to be optimized are computed analytically and provide exact search directions for an optimization process that uses only input-output data measured from the dynamic system to be modeled. Computational experiments are presented to illustrate the benefits of the proposed approach when modeling nonlinear systems. [ABSTRACT FROM AUTHOR]

Stochastic Trust-Region Response-Surface method (STRONG) is a new response-surface-based framework for simulation optimization. The appeal of STRONG lies in that it preserves the advantages, yet eliminates the disadvantages, of traditional response surface methodology (RSM) that has been used for more than 50 years. Specifically, STRONG does not require human involvement in the search process and can guarantee to converge to the true optimum with probability one (w.p.1). In this paper, we propose an improved framework, called STRONG-X, that enhances the efficiency and efficacy of STRONG to widen its applicability to more practical problems. For efficiency improvement, STRONG-X includes a newly-developed experimental scheme that consists of construction of optimal simulation designs and an assignment strategy for random number streams to obtain computational gains. For efficacy improvement, a new variant, called STRONG-XG, is developed to achieve convergence under generally-distributed responses, as opposed to STRONG and STRONG-X where convergence is guaranteed only when the response is normal. An extensive numerical study is conducted to evaluate the efficiency and efficacy of STRONG-X and STRONG-XG. Moreover, two illustrative examples are provided to show the viability of STRONG-X and STRONG-XG in practical settings. [ABSTRACT FROM AUTHOR]

This paper is concerned with a multi-period portfolio management problem over a finite horizon. The objective is to seek the optimal investment policy series which maximizes the weighted sum of a linear combination of the expected return and the variance of portfolio over all the investment periods. This formulation enables the investor to adjust weights for any period and have full freedom and control over their best tradeoff between return and risk over each period. We show that such a problem is a convex quadratic programming problem in terms of the decision variables, regardless of price dynamic nature (either linear or nonlinear cases). By solving the stationary equation directly, an optimal solution is developed for its original problem without using embedding method. The solution is simplified for a general linear price model with high-order and coupled asset dynamics and shown to be implementable with historical price data. Simulation is carried out on USA and China stock markets with real data, which demonstrates feasibility and better performance of the proposed solution than the special case considered in the literature. In particular, the proposed solution with suitable nonzero weights on intermediate time periods offers higher return at the same risk level, compared with one involving the terminal wealth only in the objective function. [ABSTRACT FROM AUTHOR]

This paper is concerned with the use of \BBH2-control performance metrics in finding optimal actuator locations for a class of infinite-dimensional systems. Conditions that guarantee convergence of the optimal actuator location for the finite-dimensional representation of the infinite-dimensional system to the optimal actuator location of the original infinite-dimensional system are provided. Minimizing an \BBH2-performance index, parameterized by the possible actuator locations and disturbance functions, leads to optimal locations that in addition to enhanced performance, also provide robustness with respect to the spatial distribution of disturbances. Numerical studies for a cantilever beam and a diffusion problem are used to demonstrate the effects of the spatial distribution of disturbances on the optimal actuator location. The effect of the diffusivity parameter on the best actuator locations is also considered. [ABSTRACT FROM AUTHOR]

In this paper, a new identification method for large heterogeneous spatially interconnected systems is presented. A string of different systems in state-space representation is considered. The proposed algorithm optimizes the Output-Error of the global system by using the Steepest-Descent and the Gauss-Newton methods. The main contribution of this work is that both the Jacobian and the Hessian matrix can be entirely captured by using Sequentially Semi-Separable (SSS) matrices. Therefore, all the computations in the optimization routine can be performed with complexity that is linear in the number of subsystems. This fact permits to obtain models for large interconnected systems at low computational cost. Finally, a numerical example is presented in order to show the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This paper presents one-layer projection neural networks based on projection operators for solving constrained variational inequalities and related optimization problems. Sufficient conditions for global convergence of the proposed neural networks are provided based on Lyapunov stability. Compared with the existing neural networks for variational inequalities and optimization, the proposed neural networks have lower model complexities. In addition, some improved criteria for global convergence are given. Compared with our previous work, a design parameter has been added in the projection neural network models, and it results in some improved performance. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural networks. [ABSTRACT FROM AUTHOR]

In this paper, a control technique named optimized backstepping is first proposed by implementing tracking control for a class of strict-feedback systems, which considers optimization as a design philosophy of the high-order system control. The basic idea is that designing the actual and virtual controls of backstepping is the optimized solutions of the corresponding subsystems so that overall control of the high-order system is optimized. In general, optimization control is designed based on the solution of Hamilton–Jacobi–Bellman equation, but solving the equation is very difficult due to the inherent nonlinearity and intractability. In order to overcome the difficulty, the neural network (NN)-based reinforcement learning strategy of actor–critic architecture is used. In every backstepping step, the actor and critic NNs are constructed for executing control behavior and evaluating control performance, respectively. According to the Lyapunov stability theorem, it is proven that the desired control performance can be obtained. Finally, a simulation example is carried out to further demonstrate the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

Approximate linear programming (ALP) and its variants have been widely applied to Markov decision processes (MDPs) with a large number of states. A serious limitation of ALP is that it has an intractable number of constraints, as a result of which constraint approximations are of interest. In this paper, we define a linearly relaxed approximation linear program (LRALP) that has a tractable number of constraints, obtained as positive linear combinations of the original constraints of the ALP. The main contribution is a novel performance bound for LRALP. [ABSTRACT FROM PUBLISHER]

Frequency control rebalances supply and demand while maintaining the network state within operational margins. It is implemented using fast ramping reserves that are expensive and wasteful, and which are expected to become increasingly necessary with the current acceleration of renewable penetration. The most promising solution to this problem is the use of demand response, i.e., load participation in frequency control. Yet it is still unclear how to efficiently integrate load participation without introducing instabilities and violating operational constraints. In this paper, we present a comprehensive load-side frequency control mechanism that can maintain the grid within operational constraints. In particular, our controllers can rebalance supply and demand after disturbances, restore the frequency to its nominal value, and preserve interarea power flows. Furthermore, our controllers are distributed (unlike the currently implemented frequency control), can allocate load updates optimally, and can maintain line flows within thermal limits. We prove that such a distributed load-side control is globally asymptotically stable and robust to unknown load parameters. We illustrate its effectiveness through simulations. [ABSTRACT FROM PUBLISHER]

Traditionally, the design of extremum seeking algorithm treats the system as essentially a black-box, which for many applications means disregarding known information about the model structure. In contrast to this approach, there have been recent examples where a known plant structure with uncertain parameters has been used in the online optimization of plant operation. However, the results for these approaches have been restricted to specific classes of plants and optimization algorithms. This paper seeks to provide general results and a framework for the design of extremum seekers applied to systems with parameter uncertainties. General conditions for an optimization method and a parameter estimator are presented so that their combination guarantees convergence of the extremum seeker for both static and dynamic plants. Tuning guidelines for the closed loop scheme are also presented. The generality and flexibility of the proposed framework is demonstrated through a number of parameter estimators and optimization algorithms that can be combined to obtain extremum seeking. Examples of anti-lock braking and model reference adaptive control are used to illustrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

This paper studies cluster synchronization in dynamical networks. A class of cooperative dynamical networks that exhibit clustering in their asymptotic behavior is analyzed. The network nodes are equipped with heterogeneous dynamics and interact with a nonlinear and saturated interaction rule. It is proven that cluster synchronization appears asymptotically independent of the initial conditions. The clustering behavior of the dynamic network is shown to correspond to the solution of a static saddle-point problem, enabling a precise characterization of the clustering structure. We show how the clustering structure depends on the relation between the underlying graph, the node dynamics, and the saturation level of the interactions. This interpretation leads to deeper combinatorial insights related to clustering, including a generalization of classical network partitioning problems such as the inhibiting bisection problem, the min s-t-cut problem, and hierarchical clustering analysis. The theoretical results are applied for the analysis of a test-case network, inspired by the IEEE 30-bus system. [ABSTRACT FROM AUTHOR]

This paper focuses on addressing the robust source prediction problem in wireless sensor networks (WSNs). In particular, the nonlinear interaction of multiple sources is handled by the fuzzy interpolation technique, which has not been fully investigated in networked data fusion systems yet. In this study, no knowledge of the joint distribution of sensor observations is assumed. A robust H\infty design using fuzzy interpolation from a game theory viewpoint is proposed. The fuzzy approximation error for the nonlinear dynamics of multiple sources is incorporated into the design. In evaluation, a theoretical analysis on the performance of the proposed multisource prediction method is provided. [ABSTRACT FROM PUBLISHER]

A technique is introduced for identifying uncertain and/or unknown parameters of chaotic and hyper-chaotic systems via using a synchronization-based parameter observer. The proposed technique is based on designing a state feedback controller and then solving for the unknown parameters using some simple parameter adaptive laws that require access to some or all of the states depending on the dynamical model of the systems. Two detailed cases utilizing chaotic and hyper-chaotic systems are used to exemplify the proposed observer when partial identification of the unknown parameters is considered, where the results demonstrated the effectiveness of the proposed method via comparing it with other methods reported in the literature, Furthermore, the feasibility of the designed observer on unknown-structure model and complex-variable model are verified through the theoretical analyses and simulation results, and the advantages and limitations of the synchronization-based parameter observer approach are discussed in detail. [ABSTRACT FROM AUTHOR]

In the design of sigma-delta modulators ($\Sigma \Delta $ Ms), different variables need to be optimized together in order to maximize the performance. This design task has the added difficulty of dealing with the non-linear behavior of the quantizer. Although a linearized model of the quantizer can be used, this may result in significant discrepancies between the predicted and actual behavior of the $\Sigma \Delta \text{M}$. To better predict the behavior of a given design, we propose a design methodology for $\Sigma \Delta $ Ms based on a genetic algorithm (GA) that uses both linear equations and simulations. In order to reduce the computation time, the design solution is initially evaluated using equations and only if the performance is deemed good enough, it is subjected to a more refined simulation. This more precise simulation takes into account thermal noise, finite output swing, and gain (among other non-idealities) of the building blocks of the modulator. Moreover, Monte Carlo (MC) analyses are performed during the optimization in order to assess the sensitivity to component variations of the solutions. In order to demonstrate the validity and robustness of the proposed optimization methodology, several $\Sigma \Delta $ Ms designs are presented, together with the corresponding measured results. [ABSTRACT FROM AUTHOR]

We address the optimal control of level sets associated with the solution of the normal flow equation. The problem consists in finding the normal velocity to the front described by a certain level set in such a way to minimize a given cost functional. First, the considered problem is shown to admit a solution on a suitable space of functions. Then, since in general it is difficult to solve it analytically, an approximation scheme that relies on the extended Ritz method is proposed to find suboptimal solutions. Specifically, the control law is forced to take on a neural structure depending nonlinearly on a finite number of parameters to be tuned, i.e., the neural weights. The selection of the optimal weights is performed with two different approaches. The first one employs classical line-search descent methods, while the second one is based on a quasi-Newton optimization that can be regarded as neural learning based on the extended Kalman filter. Compared with line-search methods, such an approach reveals to be successful with a reduced computational effort and an increased robustness with respect to the trapping into local minima, as confirmed by simulations in both two and three dimensions. [ABSTRACT FROM AUTHOR]

This paper focuses on optimal and receding horizon control of a class of hybrid dynamical systems, called Discrete Hybrid Stochastic Automata (DHSA), whose discrete-state transitions depend on both deterministic and stochastic events. A finite-time optimal control approach “optimistically” determines the trajectory that provides the best tradeoff between tracking performance and the probability of the trajectory to actually execute, under possible chance constraints. The approach is also robustified, less optimistically, to ensure that the system satisfies a set of constraints for all possible realizations of the stochastic events, or alternatively for those having enough probability to realize. Sufficient conditions for asymptotic convergence in probability are given for the receding-horizon implementation of the optimal control solution. The effectiveness of the suggested stochastic hybrid control techniques is shown on a case study in supply chain management. [ABSTRACT FROM AUTHOR]

We study the stochastic control problems with degenerate semimartingales and nonsmooth value functions. We first derive the optimality conditions for finite horizon problems with nonsmooth value functions and singular control of infinite horizon discounted problems. Then, we show that the local time at the degenerate point is zero, so if the value function has nonsmooth first-order derivatives at the degenerate points of all policies, the nonsmoothness can be ignored and the optimality equation for stochastic control, including optimal stopping, is simply the classical Hamilton–Jacobi–Bellman equation at the smooth points. Furthermore, we model the singular control by the reflecting force in the Skorokhod problem and show that there are two classes of degenerate points; in the first class, singular control at a degenerate point can be treated in the same way as regular control; and in the second class, the density of the singular control force may be of the order (\sqrtdt)^1+\gamma , $0<\gamma <1$, with $\gamma =0$ for nondegenerate points. We apply the direct-comparison-based approach to derive all the results. In all the analysis, viscosity solution is not needed. [ABSTRACT FROM PUBLISHER]