Global asymptotic stability analysis of nonlinear differential equations in hybrid bidirectional associative memory neural networks with distributed time-varying delays

Authors:
Yongqiang Zhou;Shouming Zhong;Mao Ye;Zhiyuan Shi
Affiliations:
School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China;School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China;School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China
Venue:
Neurocomputing
Year:
2009

Citing 2
Cited 1

Matrix analysis

Matrix analysis
Bidirectional associative memories

IEEE Transactions on Systems, Man and Cybernetics

New results on stability for a class of neural networks with distributed delays and impulses

Neurocomputing

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Abstract

In this paper, the problem on an unique equilibrium solution of the bidirectional associative memory neural networks with distributed time-varying delays is investigated via Lyapunov stability theory. By using topological degree theory and M-matrix theory, several sufficient conditions ensuring the existence, uniqueness and global asymptotic stability of equilibrium solution are derived. An illustrative example is given to demonstrate the effectiveness of the obtained results.