Simplifying a neuro-fuzzy model
| ISSN: |
1573-773X
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|---|---|
| Keywords: |
fuzzy systems ; neural networks ; neuro-fuzzy modeling
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| Source: |
Springer Online Journal Archives 1860-2000
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| Topics: |
Computer Science
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| Notes: |
Abstract Neuro-fuzzy modeling allows a fuzzy system to be refined by neural training, thus avoiding lenghty trial-and-error phases in defining both membership functions and inference rules. An approach to obtain simple neuro-fuzzy models is proposed, which reduces the number of rules by means of a systematic procedure that consists in successively removing a rule and updating the remaining rules in such a way that the overall input-output behavior is kept approximately unchanged over the entire training set. A formulation of the proper update is described and a criterion for choosing the rules to be removed is also provided. Initial experimental results show the effectiveness of the proposed method in reducing the complexity of a neuro-fuzzy system by using its input-output data.
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| Type of Medium: |
Electronic Resource
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| URL: |