New genetic-based approach to generate fuzzy rules from numerical data
| Autor: | Shi-Yu Sun, Jun Zhu, Run-Sheng Yang |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Adaptive neuro fuzzy inference system
Fuzzy classification Neuro-fuzzy business.industry Machine learning computer.software_genre Defuzzification Fuzzy logic Fuzzy number Fuzzy set operations Fuzzy associative matrix ComputingMethodologies_GENERAL Data mining Artificial intelligence business computer Mathematics |
| Zdroj: | Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393). |
| DOI: | 10.1109/wcica.2000.862777 |
| Popis: | Optimization of fuzzy logic controller by genetic algorithm is a very active research area. This paper develops a new genetic-based method to generate fuzzy rules from numerical data; the fuzzy rules and fuzzy membership functions can be optimized simultaneously in the algorithm. An application to truck backer-upper control is presented. The performance of this new method is compared with a non-optimized method, and shows that the new method has a better performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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