Abstract
The ordinary Takagi-Sugeno (TS) fuzzy models have provided an approach to represent complex nonlinear systems to a set of linear sub-models by using fuzzy sets and fuzzy reasoning. In this paper, stochastic fuzzy Hopfield neural networks with time-varying delays (SFVDHNNs) are studied. The model of SFVDHNN is first establisbed as a modified TS fuzzy model in which the consequent parts are composed of a set of stochastic Hopfield neural networks with time-varying delays. Secondly, the global exponential stability in the mean square for SFVDHNN is studied by using the Lyapunov-Krasovskii approach. Stability criterion is derived in terms of linear matrix inequalities (LMIs), which can be effectively solved by some standard numerical packages. © 2005 IEEE.
| Original language | English |
|---|---|
| Pages (from-to) | 251-255 |
| Journal | IEEE Transactions on Circuits and Systems II: Express Briefs |
| Volume | 52 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - May 2005 |
Research Keywords
- Fuzzy systems
- Hopfield neural networks
- Stability
- Stochastic systems
- Time-varying delay systems
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