Fuzzy rule-based networks for control

Charles M. Higgins and Rodney M. Goodman Abstract| We present a method for the learning of fuzzy logic membership functions and rules to approximate a numerical function from a set of examples of the function's independent variables and the resulting function value. This method uses a three-step approach to building a complete function approximation system: rst, learning the membership functions and creating a cell-based rule representation; second, simplifying the cell-based rules using an informationtheoretic approach for inductionof rules from discrete-valued data; and nally, constructing a computational (neural) network to compute the function value given its independent variables. This function approximation system is demonstrated with a simple control example: learning the truck and trailer backer-upper control system.