| Article ID: | iaor200914 |
| Country: | United Kingdom |
| Volume: | 29 |
| Issue: | 1 |
| Start Page Number: | 19 |
| End Page Number: | 40 |
| Publication Date: | Jan 2008 |
| Journal: | Optimal Control Applications & Methods |
| Authors: | Khellaf A., Soukkou A., Leulmi S. |
| Keywords: | fuzzy sets, heuristics: genetic algorithms, neural networks |
Designing an effective criterion/learning to find the best rule and optimal structure is a major problem in the design process of fuzzy neural controller. In this paper, we introduce a new robust model of Takagi Sugeno fuzzy logic controller. A hybrid learning algorithm, called hybrid approach to fuzzy supervised learning, which combines the genetic algorithm and gradient descent technique is proposed for constructing an efficient and robust fuzzy neural network controller.