Fuzzy rule-based networks for control
We present a method for learning 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: first, learning the membership functions and creating a cell-based rule representation; second, simplifying the cell-based rules using an information-theoretic approach for induction of rules from discrete-valued data; and, finally, 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.
© 1994 IEEE. Manuscript received December 2, 1992; revised March 8, 1993. This work was supported in part by Pacific Bell, and in part by DARPA and ONR under Grant N00014-92-J-1860.
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