Abstract
In this paper, we derive a general sufficient condition ensuring global exponential convergence of Cohen-Grossberg neural networks with time delays by constructing a novel Lyapunov functional and smartly estimating its derivative. The proposed condition is related to the convex combinations of the column-sum and the row-sum of the connection matrices and also relaxes the constraints on the network coefficients. Therefore, the proposed condition generalizes some previous results in the literature.
| Original language | English |
|---|---|
| Pages (from-to) | 1694-1696 |
| Number of pages | 3 |
| Journal | IEEE Transactions on Neural Networks |
| Volume | 16 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 1 Nov 2005 |
Keywords
- Cohen-Grossberg neural networks
- Global exponential stability
- Time delay
ASJC Scopus subject areas
- Control and Systems Engineering
- Theoretical Computer Science
- Electrical and Electronic Engineering
- Artificial Intelligence
- Computational Theory and Mathematics
- Hardware and Architecture
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