Abstract
The problem of exponential stability of multiple equilibria in recurrent neural networks with time-varying delays and concave-convex characteristics is addressed in this paper. The focus is placed upon derivation of some sufficient conditions under which an neural network of order n can have (2k + 2m - 1) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> equilibrium points with (k + m) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> of them having local exponential stability. The new results represent important extensions of the existing results on multistability of delayed recurrent neural networks.
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