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Further results on exponential stability of discrete-time BAM neural networks with time-varying delays. (English) Zbl 1367.93571

Summary: This paper is concerned with the exponential stability for the discrete-time bidirectional associative memory neural networks with time-varying delays. Based on Lyapunov’s 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.

MSC:

93D20 Asymptotic stability in control theory
93D05 Lyapunov and other classical stabilities (Lagrange, Poisson, \(L^p, l^p\), etc.) in control theory
93C55 Discrete-time control/observation systems
92B20 Neural networks for/in biological studies, artificial life and related topics
37K45 Stability problems for infinite-dimensional Hamiltonian and Lagrangian systems
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