Abstract
In this paper, we propose some new results on stability properties of Takagi-Sugeno fuzzy Hopfield neural networks with time-delay. Based on Lyapunov stability theory, a new learning law is derived to guarantee passivity and asymptotical stability of Takagi-Sugeno fuzzy Hopfield neural networks. Furthermore, a new condition for input-to-state stability (ISS) is established. Illustrative examples are given to demonstrate the effectiveness of the proposed results.
Original language | English |
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Pages (from-to) | 100-111 |
Number of pages | 12 |
Journal | Fuzzy Sets and Systems |
Volume | 179 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2011 Sept 16 |
Externally published | Yes |
Keywords
- Input-to-state stability (ISS)
- Learning
- Lyapunov stability theory
- Neuro-fuzzy systems
- Passivity
ASJC Scopus subject areas
- Logic
- Artificial Intelligence