Title :
On exponential stability of delayed neural networks with globally Lipschitz continuous activation functions
Author :
Sun, Changyin ; Feng, Chun-Bo
Author_Institution :
Res. Inst. of Autom., Southeast Univ., Nanjing, China
Abstract :
In this paper, based on globally Lipschitz continuous activation functions, new conditions ensuring existence, uniqueness and global exponential stability of the equilibrium point of delayed neural networks are obtained. The delayed Hopfield network and bidirectional associative memory network are special cases of the network model considered in this paper. So this work gives some improvements to the previous ones.
Keywords :
Hopfield neural nets; associative processing; asymptotic stability; content-addressable storage; Hopfield neural network; bidirectional associative memory network; delayed neural networks; equilibrium point; global exponential stability; globally Lipschitz continuous activation functions; Associative memory; Asymptotic stability; Automation; Convergence; Delay effects; Electronic mail; Hopfield neural networks; Neural networks; Stability analysis; Sun;
Conference_Titel :
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
Print_ISBN :
0-7803-7268-9
DOI :
10.1109/WCICA.2002.1021425
