Abstract
This paper is concerned with the problem of global robust asymptotical stability of the equilibrium point for bidirectional associative memory (BAM) neural networks. The activation functions are assumed to be neither differentiable nor strict monotonic and the delays are time-varying. Furthermore, based on the approach of linear matrix inequality (LMI), a new inequality and Lyapunov–Krasovskii functional are applied to derive the results of robust asymptotical stability. Also, a simulation example is presented to demonstrate the effectiveness and applicability of our results.
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