H∞ State Estimation for Discrete-Time Delayed Systems of the Neural Network Type With Multiple Missing Measurements

IEEE Transactions on Neural Networks and Learning Systems
Meiqin Liu, Haiyang Chen

Abstract

This paper investigates the H∞ state estimation problem for a class of discrete-time nonlinear systems of the neural network type with random time-varying delays and multiple missing measurements. These nonlinear systems include recurrent neural networks, complex network systems, Lur'e systems, and so on which can be described by a unified model consisting of a linear dynamic system and a static nonlinear operator. The missing phenomenon commonly existing in measurements is assumed to occur randomly by introducing mutually individual random variables satisfying certain kind of probability distribution. Throughout this paper, first a Luenberger-like estimator based on the imperfect output data is constructed to obtain the immeasurable system states. Then, by virtue of Lyapunov stability theory and stochastic method, the H∞ performance of the estimation error dynamical system (augmented system) is analyzed. Based on the analysis, the H∞ estimator gains are deduced such that the augmented system is globally mean square stable. In this paper, both the variation range and distribution probability of the time delay are incorporated into the control laws, which allows us to not only have more accurate models of the real physical syste...Continue Reading

References

Oct 19, 2010·Neural Networks : the Official Journal of the International Neural Network Society·Haibo Bao, Jinde Cao
Jun 1, 2013·ISA Transactions·Christopher D Karlgaard, Haijun Shen
Jun 13, 2013·IEEE Transactions on Cybernetics·Yan ZhaoHamid Reza Karimi
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Bo ShenHuisheng Shu
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Junzhi YuJianwei Zhang
Oct 8, 2014·IEEE Transactions on Neural Networks and Learning Systems·Meiqin LiuWeihua Sheng

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Citations

Sep 19, 2016·Neural Networks : the Official Journal of the International Neural Network Society·Sanbo DingHuaguang Zhang
Oct 9, 2016·Neural Networks : the Official Journal of the International Neural Network Society·Li ShengFuad E Alsaadi
May 26, 2017·IEEE Transactions on Neural Networks and Learning Systems·Hongli DongWeijian Ren
Jul 12, 2018·IEEE Transactions on Neural Networks and Learning Systems· Zheng-Guang Wu Hongye Su

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