On Stability of Neural Networks by a Lyapunov Functional-Based Approach
| Autor: | Shouming Zhong, Jun Xu, Yong-Yan Cao, Daoying Pi |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Quantitative Biology::Neurons and Cognition
Exponential stability Artificial neural network Lyapunov functional Computer science Control theory Cellular neural network Computer Science::Neural and Evolutionary Computation Stability (learning theory) Monotonic function Electrical and Electronic Engineering |
| Zdroj: | IEEE Transactions on Circuits and Systems I: Regular Papers. 54:912-924 |
| ISSN: | 1057-7122 |
| Popis: | In this paper, a new Lyapunov functional-based method is proposed for the stability analysis of delayed cellular neural networks (DCNN). Global exponential stability conditions are obtained for the general DCNN, the Hopfield neural networks (HNNs), and delayed HNNs with monotonic nondecreasing and nonconstant activation functions |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|