A Competitive and Heuristic Error-Feedback Learning for Fuzzy Modeling
碩士 === 國立中央大學 === 機械工程研究所 === 87 === This article describes a new type of fuzzy system with extrapolation capability to extract MISO fuzzy if-then rules from input-output sample data through learning. The proposed model inherits many merits from Sugeno-type models and their variations th...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
1999
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| Online Access: | http://ndltd.ncl.edu.tw/handle/49286730661173909927 |
| Summary: | 碩士 === 國立中央大學 === 機械工程研究所 === 87 === This article describes a new type of fuzzy system with extrapolation capability to extract MISO fuzzy if-then rules from input-output sample data through learning. The proposed model inherits many merits from Sugeno-type models and their variations that can be found in many fuzzy modeling literature. The model is shown to be an universal approximator of a non-linear
mapping. A heuristic error-feedback learning algorithm associated with the new model is suggested. Based on which, the estimator is shown to have a self-adjusting step when approaching a minimum.The power of the algorithm is demonstrated by four numerical examples. Comparison shows that the suggested approach can produce a fuzzy model with a simple methodology in a sense that no other complex optimization techniques are required.
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