A genetic-algorithm-and-table-rotating-based method for optimizing fuzzy control rules

It has been demonstrated many times in practice that fuzzy logic controllers have an important role in rule-based expert systems. However, it is essential for a fuzzy logic controller to have an appropriate set of rules to perform at the desired level. The linguistic structure of the fuzzy logic controller allows a tentative linguistic policy to be used as an initial rule base. At the design stage, if one can assemble a reasonably good collection of rules, it may then be possible to tune these rules to improve the controller performance. In the paper, a genetic-algorithm-and-table-rotating-based method for optimizing fuzzy control rules and the simulation result are presented. Finally, the results are discussed.