A study of exponential stability of multiple equilibria in delayed recurrent neural networks

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)ⁿ equilibrium points with (k + m)ⁿ of them having local exponential stability. The new results represent important extensions of the existing results on multistability of delayed recurrent neural networks.