Automatic Design and Optimization of Fuzzy Inference Systems

Abstract

Fuzzy inference systems have found a very spread application field, especially in areas, which interact with humans. However, they lack any self-learning capabilities for design of their knowledge bases. Beside such means as neural networks and interpolation methods also genetic algorithms are used in this area. First of all the conventional approaches of genetic algorithms have found use in rule-based fuzzy inference systems. In addition, other approaches, as parts of a broader group of evolutionary algorithms, like particle swarm optimization and simulated annealing were applied for this area. Finally, various other promising approaches like fuzzy cognitive maps were adapted for fuzzy logic, too. Therefore, the structure of this chapter has three basic parts and it deals at first with adaptation and knowledge acquisition possibilities of fuzzy inference systems in general. Consecutively, methods of using genetic algorithms for the design of rule-based fuzzy inference systems are described. In the last part the scope of fuzzy cognitive maps is analysed and some adaptation approaches based on evolutionary algorithms are introduced. © Springer-Verlag Berlin Heidelberg 2013.