Global asymptotic stability analysis of bidirectional associative memory neural networks with time delays


Arik S.

IEEE TRANSACTIONS ON NEURAL NETWORKS, vol.16, no.3, pp.580-586, 2005 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 16 Issue: 3
  • Publication Date: 2005
  • Doi Number: 10.1109/tnn.2005.844910
  • Title of Journal : IEEE TRANSACTIONS ON NEURAL NETWORKS
  • Page Numbers: pp.580-586
  • Keywords: delayed neural networks, equilibrium and stability analysis, Lyapunov functionals, EXPONENTIAL STABILITY, EQUILIBRIUM-ANALYSIS, SUFFICIENT CONDITION, PERIODIC-SOLUTIONS, CRITERIA

Abstract

This paper presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with distributed time delays. The results impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all continuous nonmonotonic neuron activation functions. It is shown that in some special cases of the results, the stability criteria can be easily checked. Some examples are also given to compare the results with the previous results derived in the literature.