Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/100972
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Type: Journal article
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
Author: Mathiyalagan, K.
Su, H.
Shi, P.
Sakthivel, R.
Citation: IEEE Transactions on Cybernetics, 2015; 45(4):676-687
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2015
ISSN: 2168-2267
2168-2275
Statement of
Responsibility: 
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.
RMID: 0030025345
DOI: 10.1109/TCYB.2014.2332356
Grant ID: http://purl.org/au-research/grants/arc/DP140102180
Appears in Collections:Electrical and Electronic Engineering publications

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