Global asymptotic stability analysis of bidirectional associative memory neural networks with constant time delays - Arik, S; Tavsanoglu, V (ELSEVIER, 2005)
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 fixed time delays. The results impose constraint conditions on the network parameters of neural system independent of the delay parameters. The results are applicable to all continuous non-monotonic neuron activation functions. The results are also compared with the previously reported results in the literature, implying that the results obtained in this paper provide one more set of criteria for determining the stability of bidirectional associative memory neural networks with time delays. (c) 2005 Elsevier B.V. All rights reserved.
Keywords: global asymptotic stability; Lyapunov functionals; Bi-directional associative memory neural networks; time delays; EXPONENTIAL STABILITY; SUFFICIENT CONDITION; CONVERGENCE ANALYSIS; QUALITATIVE-ANALYSIS; EQUILIBRIUM-ANALYSIS; CRITERIA; Computer Science