Fuzzy classifier system using hyper-cone membership functions and rule reduction techniques

Proposes a fuzzy classifier system (FCS) using hyper-cone membership functions and rule reduction techniques. The FCS can generate excellent rules which have the best number of rules and the best location and shape of membership functions. The hyper-cone membership function is expressed by a kind of radial basis function, and its fuzzy rule can be flexibly located in input and output spaces. The rule reduction technique adopts a decreasing method by merging the two appropriate rules. We apply the FCS to a fuzzy rule generation for line pursuit control.