Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published February 1994 | Published
Journal Article Open

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.

Additional Information

© 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.

Attached Files

Published - 00273129.pdf


Files (712.7 kB)
Name Size Download all
712.7 kB Preview Download

Additional details

August 20, 2023
August 20, 2023