Global Convergence Analysis of Delayed Bidirectional Associative Memory Neural Networks

Abstract

This paper studies the stability properties of a more general class of bidirectional associative memory (BAM) neural networks with constant time delays. Without assuming the symmetry of the interconnection matrices, and monotonicity and differentiability of the activation functions, we derive a new sufficient condition for the global asymptotic stability of the equilibrium point for bidirectional associative memory neural networks. The obtained results are independently of the delay parameters and can be easily verified. The results are also compared with the previous results derived in the literature.