Intermittent Stabilization of Fuzzy Competitive Neural Networks with Reaction Diffusions
Document Type
Article
Date of Original Version
1-1-2020
Abstract
This paper investigates the global exponential stability and stabilization problems for a class of Takagi-Sugeno (T-S) fuzzy competitive neural networks (NNs). In the considered model, we introduce the T-S fuzzy rule to describe the parametric switching causing by complexity and the vagueness in practical environment. Besides, the effects of reaction diffusions and distributed delays, which inherently exist in circuits of NNs, are also taken into consideration. By using the Lyapunov functional theory and Green formula, several stability criteria in terms of p-norm are established for the uncompensated fuzzy competitive NNs. Moreover, by designing a fuzzy intermittent controller, the corresponding stabilizability criteria in terms of p-norm are derived. We also carry out some discussions and comparisons to further show the less conservativeness and wide applicability of the main theorems. Finally, several examples are presented to verify the obtained results.
Publication Title, e.g., Journal
IEEE Transactions on Fuzzy Systems
Citation/Publisher Attribution
Wang, Leimin, Haibo He, and Zhigang Zeng. "Intermittent Stabilization of Fuzzy Competitive Neural Networks with Reaction Diffusions." IEEE Transactions on Fuzzy Systems (2020). doi: 10.1109/TFUZZ.2020.2999041.