Delay-dependent exponential stability analysis of bi-directional associative memory neural networks with time delay: an LMI approach

Chuandong Li, Xiaofeng Liao and Rong Zhang

Chaos, Solitons & Fractals, 2005, vol. 24, issue 4, 1119-1134

Abstract: For bi-directional associative memory (BAM) neural networks (NNs) with different constant or time-varying delays, the problems of determining the exponential stability and estimating the exponential convergence rate are investigated in this paper. An approach combining the Lyapunov–Krasovskii functional with the linear matrix inequality (LMI) is taken to study the problems, which provide bounds on the interconnection matrix and the activation functions, so as to guarantee the system’s exponential stability. Some criteria for the exponential stability, which give information on the delay-dependent property, are derived. The results obtained in this paper provide one more set of easily verified guidelines for determining the exponential stability of delayed BAM (DBAM) neural networks, which are less conservative and less restrictive than the ones reported so far in the literature. Some typical examples are presented to show the application of the criteria obtained in this paper.

Keywords: Bi-directional associative memory neural network; Time delays; Exponential stability; Linear matrix inequality; Lyapunov–Krasovskii functional (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:24:y:2005:i:4:p:1119-1134

DOI: 10.1016/j.chaos.2004.09.052

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