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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kasabov, N.K. |
| Copyright Year | 1993 |
| Description | Author affiliation: Dept. of Inf. Sci., Otago Univ., Dunedin, New Zealand (Kasabov, N.K.) |
| Abstract | Presents a method for learning fuzzy rules through training a neural network with the backpropagation algorithm. Membership functions for the fuzzy concepts participating in the rules can also be learned through the proposed scheme. The learned fuzzy rules can then be implemented in a fuzzy inference machine, and a function which approximates the real goal function between the independent input variables and the dependent output variables can be derived. This approach has been compared with the regression analysis approach on the example of a simple forecasting problem. Both neural networks and fuzzy systems have shown superior accuracy. Mixing of the three approaches is also discussed.< |
| Starting Page | 137 |
| Ending Page | 139 |
| File Size | 331885 |
| Page Count | 3 |
| File Format | |
| ISBN | 0818642602 |
| DOI | 10.1109/ANNES.1993.323062 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1993-11-24 |
| Publisher Place | New Zealand |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fuzzy neural networks Neural networks Fuzzy sets Regression analysis Temperature Backpropagation algorithms Feedforward neural networks Information science Input variables Problem-solving |
| Content Type | Text |
| Resource Type | Article |
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