Most of existing methods in system identification with possible exception of those for linear systems are off-line in nature, and hence are nonrecursive. This paper demonstrates the recent progress in recursive system...
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Most of existing methods in system identification with possible exception of those for linear systems are off-line in nature, and hence are nonrecursive. This paper demonstrates the recent progress in recursive system identification. The recursive identification algorithms are presented not only for linear systems (multivariate ARMAX systems) but also for nonlinear systems such as the Hammerstein and Wiener systems, and the nonlinear ARX systems. The estimates generated by the algorithms are online updated and converge a.s. to the true values as time tends to infinity.
Traditional convergence theory of self-tuning regulators requires boundedness of the conditional variances of the systems noise processes. However, this requirement cannot be satisfied for many practical models such t...
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ISBN:
(纸本)9781538629185
Traditional convergence theory of self-tuning regulators requires boundedness of the conditional variances of the systems noise processes. However, this requirement cannot be satisfied for many practical models such the well-known ARCH(Autoregressive Conditional Heteroscedasticity) model in economic systems. The aim of this paper is to provide a convergence theory of self-tuning regulators for linear uncertain systems with conditional heteroscedastic noises, where the conditional variance is unbounded. To be specific, we consider weighted least-squares-based self-tuning regulators, and establish both the global stability and the optimality of tracking under some natural conditions on the system models as well as on the conditional heteroscedastic noises. To the best of the authors’ knowledge, this is the first paper that investigates this kind of problems with a convergence theory, and makes the self-tuning regulators applicable to systems with noised modeled by ARCH.
In this paper, we consider the diffusion adaptive filters where a set of sensors is required to collectively estimate time-varying signals (or parameters) from noisy measurements in a way of information diffusion. We ...
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The ring structure and Boolean variables are investigated and they are used to define the product of Boolean matrices. The logical operators have also been extended to the operators on Boolean matrices. Based on these...
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Using semi-tensor product of matrices, a matrix expression for multivalued logic is proposed, where a logical variable is expressed as a vector, and a logical function is expressed as a multilinear mapping. Under this...
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First, it is shown that identification of many block-oriented nonlinear systems can be reduced to estimating parameters even if the nonlinearity is not parameterized. It is then shown that the parameter estimation pro...
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Identification of the Wiener system composed of an infinite impulse response (IIR) linear subsystem followed by a static nonlinearity is *** recursive estimates for unknown coefficients of the linear subsystem and for...
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Identification of the Wiener system composed of an infinite impulse response (IIR) linear subsystem followed by a static nonlinearity is *** recursive estimates for unknown coefficients of the linear subsystem and for the values of the nonlinear function at any fixed points are given by the stochastic approx-imation algorithms with expanding truncations (SAAWET).With the help of properties of the Markov chain connected with the linear subsystem,all estimates derived in the paper are proved to be strongly *** comparison with the existing results on the topic,the method presented in the paper simplifies the convergence analysis and requires weaker conditions.A numerical example is given,and the simulation results are consistent with the theoretical analysis.
In this note, we do something in the cooperative output regulation of linear multi-agent systems. The linear multi-agent systems which we consider is a class of leader-follower systems, only part of agents can access ...
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ISBN:
(纸本)9781479900305
In this note, we do something in the cooperative output regulation of linear multi-agent systems. The linear multi-agent systems which we consider is a class of leader-follower systems, only part of agents can access the exogenous signal. What we do is design a simple control law with a dynamic compensator to solve the cooperative output regulation problem. We consider two situations in designing control law, one is using full information of state, another is using part information.
Abstract First, it is shown that identification of many block-oriented nonlinear systems can be reduced to estimating parameters even if the nonlinearity is not parameterized. It is then shown that the parameter estim...
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Abstract First, it is shown that identification of many block-oriented nonlinear systems can be reduced to estimating parameters even if the nonlinearity is not parameterized. It is then shown that the parameter estimation problem can be transformed to seeking roots of some unknown function called as regression function. This is the topic of stochastic approximation (SA). It is demonstrated by example how to appropriately select the regression function and the corresponding observations in order the resulting observation errors to be suitable for convergence analysis. The classical Robbins-Monro algorithm in SA is modified to the stochastic approximation algorithm with expanding truncations (SAAWET), and for it a general convergence theorem is presented. The proposed approach is then applied to identify the MIMO Hammerstein systems with linear block being an ARX system whose input and output both are corrupted by random noises. The recursive algorithms are derived for estimating f ( u ) with fixed u and the matrix coefficients of the linear subsystem, where the vector function f (·) is the static nonlinearity of the system. All estimates are convergent to the true values with probability one under reasonable conditions. A numerical example shows that the simulation results are consistent with the theoretical analysis.
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