Further results on exponential stability of discrete‐time BAM neural networks with time‐varying delays

This paper is concerned with the exponential stability for the discrete‐time bidirectional associative memory neural networks with time‐varying delays. Based on Lyapunov stability theory, some novel delay‐dependent sufficient conditions are obtained to guarantee the globally exponential stability of...

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Published in:Mathematical methods in the applied sciences 2017-07, Vol.40 (11), p.4014-4027
Main Authors: Shu, Yanjun, Liu, Xinge, Wang, Fengxian, Qiu, Saibing
Format: Article
Language:English
Subjects:
Associative memory
Control systems
Delay
discrete‐time BAM neural networks
exponential stability
linear matrix inequalities (LMIs)
Lyapunov–Krasovskii functional
Neural networks
reciprocally convex approach
Recurrent neural networks
Stability
Weighting
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description This paper is concerned with the exponential stability for the discrete‐time bidirectional associative memory neural networks with time‐varying delays. Based on Lyapunov stability theory, some novel delay‐dependent sufficient conditions are obtained to guarantee the globally exponential stability of the addressed neural networks. In order to obtain less conservative results, an improved Lyapunov–Krasovskii functional is constructed and the reciprocally convex approach and free‐weighting matrix method are employed to give the upper bound of the difference of the Lyapunov–Krasovskii functional. Several numerical examples are provided to illustrate the effectiveness of the proposed method. Copyright © 2017 John Wiley & Sons, Ltd.
doi_str_mv 10.1002/mma.4281
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subjects Associative memory
Control systems
Delay
discrete‐time BAM neural networks
exponential stability
linear matrix inequalities (LMIs)
Lyapunov–Krasovskii functional
Neural networks
reciprocally convex approach
Recurrent neural networks
Stability
Weighting
title Further results on exponential stability of discrete‐time BAM neural networks with time‐varying delays
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