Stability analysis of a delayed Hopfield neural network.
In this paper, we study a class of neural networks, which includes bidirectional associative memory networks and cellular neural networks as its special cases. By Brouwer's fixed point theorem, a continuation theorem based on Gains and Mawhin's coincidence degree, matrix theory, and inequality analysis, we not only obtain some different sufficient conditions ensuring the existence, uniqueness, and global exponential stability of the equilibrium but also estimate the exponentially convergent rate. Our results are less restrictive than previously known criteria and can be applied to neural networks with a broad range of activation functions assuming neither differentiability nor strict monotonicity.
Guo
Shangjiang
S
College of Mathematics and Econometrics, Hunan University, Changsha, Hunan, People's Republic of China. shangjguo@etang.com
Huang
Lihong
L
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001703:Biophysics / Q000379:methods*; D008956:Models, Chemical; D015233:Models, Statistical; D008962:Models, Theoretical; D009415:Nerve Net; D016571:Neural Networks, Computer; D009474:Neurons / Q000473:pathology; D013997:Time Factors