About the Combination of Functional Approaches and Fuzzy Reasoning
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Information Sciences—Informatics and Computer Science: An International Journal
Computers in Industry - Special issue: Application of genetics algorithms in industry
A hierarchical fuzzy system with high input dimensions for forecasting foreign exchange rates
International Journal of Artificial Intelligence and Soft Computing
Computers in Industry - Special issue: Application of genetics algorithms in industry
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Hybrid control of a pneumatic artificial muscle (PAM) robot arm using an inverse NARX fuzzy model
Engineering Applications of Artificial Intelligence
Optical fuzzy logic systems in problems of adaptive simulation of weakly formalized processes
Journal of Computer and Systems Sciences International
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
A double axis classification of interpretability measures for linguistic fuzzy rule-based systems
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Fuzzy modeling incorporated with fuzzy d-s theory and fuzzy naive bayes
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Enhancing web server relative delay services by an integrated SA-fuzzy logic controller
International Journal of Web Engineering and Technology
| Hi-index | 0.00 |
Fuzzy logic controllers (FLCs) are gaining in popularity across a broad array of disciplines because they allow a more human approach to control. Recently, the design of the fuzzy sets and the rule base has been automated by the use of genetic algorithms (GAs) which are powerful search techniques. Though the use of GAs can produce near optimal FLCs, it raises problems such as messy overlapping of fuzzy sets and rules not in agreement with common sense. This paper describes an enhanced genetic algorithm which constrains the optimization of FLCs to produce well-formed fuzzy sets and rules which can be better understood by human beings. To achieve the above, we devised several new genetic operators and used a parallel GA with three populations for optimizing FLCs with 3×3, 5×5, and 7×7 rule bases, and we also used a novel method for creating migrants between the three populations of the parallel GA to increase the chances of optimization. In this paper, we also present the results of applying our GA to designing FLCs for controlling three different plants and compare the performance of these FLC's with their unconstrained counterparts