title: Global asymptotic stability analysis of bidirectional associative memory neural networks with time delays.
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.
authors.affiliation: Department of Computer Engineering, Istanbul University, Istanbul 34320, Turkey.
authors.forename: Sabri
authors.initials: S
authors.lastname: Arik
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016247:Information Storage and Retrieval / Q000379:methods*; D016014:Linear Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D013997:Time Factors