Title
On the global asymptotic stability independent of delay of neural networks
Document Type
Article
Publication Date
1-1-1997
Abstract
Recurrent neural networks have the potential of performing parallel computation for associative memory and optimization, which is realized by the electronic implementation of neural networks in VLSI technology. Since the time delays in real electronic implementation of neural networks are unavoidably encountered and they can cause systems to oscillate, it is thus practically important to investigate the qualitative properties of neural networks with time delays. In this paper, a class of sufficient conditions is obtained, under which neural networks are globally asymptotically stable independent of time delays.
Publication Source (Journal or Book title)
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
First Page
247
Last Page
250
Recommended Citation
Liang, X., & Yamaguchi, T. (1997). On the global asymptotic stability independent of delay of neural networks. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E80-A (1), 247-250. Retrieved from https://repository.lsu.edu/eecs_pubs/875