Evolutionary fuzzy system design and implementation

This work proposes a methodology for the design of fuzzy systems based on evolutionary computation techniques. A three-stage evolutionary algorithm that uses genetic algorithms evolves the knowledge base of a fuzzy system - rule base and parameters. The evolutionary aspect makes the design more simple and efficient, especially when compared with traditional trial and error methods. The method emphasizes interpretability so that the resulting strategy is clearly stated. An evolvable hardware platform for the synthesis of analog electronic circuits is proposed. This platform, which can be used for the implementation of the designed fuzzy system, is based on a field programmable analog array. The performance of a fuzzy system in the control of both a linear and nonlinear plant is evaluated. The results obtained with these two plants show the applicability of this hybrid model in the design of fuzzy control systems.