Published June 1992
| Published
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Learning fuzzy rule-based neural networks for function approximation
- Creators
- Higgins, C. M.
- Goodman, R. M.
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.
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.Attached Files
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Additional details
- Eprint ID
- 93849
- Resolver ID
- CaltechAUTHORS:20190314-155127145
- Army Research Office (ARO)
- DAAL03-89-K-0126
- Defense Advanced Research Projects Agency (DARPA)
- Air Force Office of Scientific Research (AFOSR)
- AFOSR-90-0199
- Created
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2019-03-14Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field