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Fuzzy rule-based networks for control

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.

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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.

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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.
Funding AgencyGrant Number
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Office of Naval Research (ONR)N00014-92-J-1860
Issue or Number:1
Record Number:CaltechAUTHORS:20190315-142400048
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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
Deposited By: George Porter
Deposited On:15 Mar 2019 21:30
Last Modified:03 Oct 2019 20:58

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