Abstract

The problem of stability of multiple equilibria is studied in this paper for two kinds of recurrent neural networks with time-varying delays and activation functions symmetrical with respect to the origin on the phase plane. Some sufficient conditions are obtained to ensure that two kinds of recurrent neural networks can have (2m + 1)n equilibrium points and (m + 1)n of them are locally exponentially stable. The derived conditions are valuable extensions to the existing results on stability of multiple equilibria for recurrent neural networks with time-varying delays in the literature.

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