Title
Absolute exponential stability of neural networks with asymmetric connection matrices
Document Type
Article
Publication Date
1-1-1997
Abstract
In this letter, the absolute exponential stability result of neural networks with asymmetric connection matrices is obtained, which generalizes the existing one about absolute stability of neural networks, by a new proof approach. It is demonstrated that the network time constant is inversely proportional to the global exponential convergence rate of the network trajectories to the unique equilibrium. A numerical simulation example is also given to illustrate the obtained analysis results.
Publication Source (Journal or Book title)
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
First Page
1531
Last Page
1533
Recommended Citation
Liang, X., & Yamaguchi, T. (1997). Absolute exponential stability of neural networks with asymmetric connection matrices. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E80-A (8), 1531-1533. Retrieved from https://repository.lsu.edu/eecs_pubs/872