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Learning fuzzy rule-based neural networks for function approximation

Higgins, C. M. and Goodman, R. M. (1992) Learning fuzzy rule-based neural networks for function approximation. In: [Proceedings 1992] IJCNN International Joint Conference on Neural Networks. Vol.1. IEEE , Piscataway, NJ, pp. 251-256. ISBN 0780305590.

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In this paper, we present a method for the induction of fuzzy logic rules to predict a numerical function from samples of the function and its dependent variables. This method uses an information-theoretic approach based on our previous work with discrete-valued data [3]. The rules learned can then be used in a neural network to predict the function value based upon its dependent variables. An example is shown of learning a control system function.

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Additional Information:© 1992 IEEE. This work was supported in part by the Army Research Office under contract number DAAL03-89-K-0126, and in part by DARPA under contract number AFOSR-90-0199.
Funding AgencyGrant Number
Army Research Office (ARO)DAAL03-89-K-0126
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Air Force Office of Scientific Research (AFOSR)AFOSR-90-0199
Record Number:CaltechAUTHORS:20190314-155127145
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Official Citation:C. M. Higgins and R. M. Goodman, "Learning fuzzy rule-based neural networks for function approximation," [Proceedings 1992] IJCNN International Joint Conference on Neural Networks, Baltimore, MD, USA, 1992, pp. 251-256 vol.1. doi: 10.1109/IJCNN.1992.287127
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:93849
Deposited By: George Porter
Deposited On:14 Mar 2019 23:23
Last Modified:16 Nov 2021 17:01

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