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|Title:||Exponential H∞ filtering for discrete-time switched neural networks with random delays|
|Other Titles:||Exponential H-infinity filtering for discrete-time switched neural networks with random delays|
|Citation:||IEEE Transactions on Cybernetics, 2015; 45(4):676-687|
|Publisher:||Institute of Electrical and Electronics Engineers|
|Kalidass Mathiyalagan, Hongye Su, Peng Shi, and Rathinasamy Sakthivel|
|Abstract:||This paper addresses the exponential H∞ filtering problem for a class of discrete-time switched neural networks with random time-varying delays. The involved delays are assumed to be randomly time-varying which are characterized by introducing a Bernoulli stochastic variable. Effects of both variation range and distribution probability of the time delays are considered. The nonlinear activation functions are assumed to satisfy the sector conditions. Our aim is to estimate the state by designing a full order filter such that the filter error system is globally exponentially stable with an expected decay rate and a H∞ performance attenuation level. The filter is designed by using a piecewise Lyapunov–Krasovskii functional together with linear matrix inequality (LMI) approach and average dwell time method. First, a set of sufficient LMI conditions are established to guarantee the exponential mean-square stability of the augmented system and then the parameters of full-order filter are expressed in terms of solutions to a set of LMI conditions. The proposed LMI conditions can be easily solved by using standard software packages. Finally, numerical examples by means of practical problems are provided to illustrate the effectiveness of the proposed filter design.|
|Keywords:||Average dwell time; exponential state estimation; H∞ filtering; random time-varying delays; switched neural networks|
|Rights:||© 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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