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. https://resolver.caltech.edu/CaltechAUTHORS:20190314-155127145
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Abstract
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.
| Item Type: | Book Section | ||||||||
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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. | ||||||||
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| DOI: | 10.1109/ijcnn.1992.287127 | ||||||||
| Record Number: | CaltechAUTHORS:20190314-155127145 | ||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20190314-155127145 | ||||||||
| 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 | ||||||||
| Collection: | CaltechAUTHORS | ||||||||
| Deposited By: | George Porter | ||||||||
| Deposited On: | 14 Mar 2019 23:23 | ||||||||
| Last Modified: | 16 Nov 2021 17:01 |
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