Higgins, Charles M. and Goodman, Rodney M. (1994) Fuzzy rule-based networks for control. IEEE Transactions on Fuzzy Systems, 2 (1). pp. 82-88. ISSN 1063-6706. https://resolver.caltech.edu/CaltechAUTHORS:20190315-142400048
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Abstract
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.
Item Type: | Article | ||||||||
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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. | ||||||||
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Issue or Number: | 1 | ||||||||
Record Number: | CaltechAUTHORS:20190315-142400048 | ||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20190315-142400048 | ||||||||
Official Citation: | C. M. Higgins and R. M. Goodman, "Fuzzy rule-based networks for control," in IEEE Transactions on Fuzzy Systems, vol. 2, no. 1, pp. 82-88, Feb. 1994. doi: 10.1109/91.273129 | ||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||
ID Code: | 93890 | ||||||||
Collection: | CaltechAUTHORS | ||||||||
Deposited By: | George Porter | ||||||||
Deposited On: | 15 Mar 2019 21:30 | ||||||||
Last Modified: | 03 Oct 2019 20:58 |
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