Delay-distribution-dependent stability of stochastic discrete-time neural networks with randomly mixed time-varying delays

Authors:
Yang Tang;Jian-an Fang;Min Xia;Dongmei Yu
Affiliations:
College of Information Science and Technology, Donghua University, Shanghai 201620, PR China and Institute of Textiles and Clothing, Hong Kong Polytechnic University, Hung Hom Kowloon, Hong Kong, ...;College of Information Science and Technology, Donghua University, Shanghai 201620, PR China;College of Information Science and Technology, Donghua University, Shanghai 201620, PR China and Institute of Textiles and Clothing, Hong Kong Polytechnic University, Hung Hom Kowloon, Hong Kong, ...;College of Information Science and Technology, Donghua University, Shanghai 201620, PR China
Venue:
Neurocomputing
Year:
2009

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Abstract

In this paper, the stability analysis problem for a new class of discrete-time neural networks with randomly discrete and distributed time-varying delays has been investigated. Compared with the previous work, the distributed delay is assumed to be time-varying. Moreover, the effects of both variation range and probability distribution of mixed time-delays are taken into consideration in the proposed approach. The distributed time-varying delays and coupling term in complex networks are considered by introducing two Bernoulli stochastic variables. By using some novel analysis techniques and Lyapunov-Krasovskii function, some delay-distribution-dependent conditions are derived to ensure that the discrete-time complex network with randomly coupling term and distributed time-varying delay is synchronized in mean square. A numerical example is provided to demonstrate the effectiveness and the applicability of the proposed method.