An Analysis of Global Stability of Takagi-Sugeno Fuzzy Cohen-Grossberg Neural Networks with Time Delays
NEURAL PROCESSING LETTERS, vol.48, no.3, pp.1693-1704, 2018 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 48 Issue: 3
- Publication Date: 2018
- Doi Number: 10.1007/s11063-018-9792-x
- Journal Name: NEURAL PROCESSING LETTERS
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.1693-1704
- Istanbul University Affiliated: Yes
Abstract
This paper deals with the problem of the global asymptotic stability of Takagi-Sugeno (T-S) fuzzy Cohen-Grossberg neural networks with multiple time delays. By using Lyapunov method and some basic properties of matrices, and employing the nondecreasing and slope-bounded activation functions, an easily verifiable sufficient criterion is derived to establish the asymptotic stability of a general class of (T-S) fuzzy Cohen-Grossberg neural networks with multiple time delays. The obtained stability condition establishes some relationships between the network parameters of the neural network model independently of the delay parameters. A numerical example is also given to illustrate the effectiveness of the theoretical results.