Further results on exponential stability of discrete‐time BAM neural networks with time‐varying delays |
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| Authors: | Yanjun Shu Xinge Liu Fengxian Wang Saibing Qiu |
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| Institution: | 1. School of Mathematics and Statistics, Central South University, Changsha, China;2. College of Mathematics and Computer Science, Hunan City University, Yiyang, China |
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| Abstract: | 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. |
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| Keywords: | discrete‐time BAM neural networks exponential stability Lyapunov– Krasovskii functional reciprocally convex approach linear matrix inequalities (LMIs) |
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