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Extracting interpretable fuzzy rules from RBF networks

Jin, Y and Sendhoff, B (2003) Extracting interpretable fuzzy rules from RBF networks Neural Processing Letters, 17 (2). pp. 149-164.

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Radial basis function networks and fuzzy rule systems are functionally equivalent under some mild conditions. Therefore, the learning algorithms developed in the field of artificial neural networks can be used to adapt the parameters of fuzzy systems. Unfortunately, after the neural network learning, the structure of the original fuzzy system is changed and interpretability, which is considered to be one of the most important features of fuzzy systems, is usually impaired. This Letter discusses the differences between RBF networks and interpretable fuzzy systems. Based on these discussions, a method for extracting interpretable fuzzy rules from RBF networks is suggested. Simulation examples are given to embody the idea of this paper

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Date : 2003
Identification Number : 10.1023/A:1023642126478
Additional Information : Copyright 2003 Kluwer Academic Publishers.The original publication is available at
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:58
Last Modified : 31 Oct 2017 14:31

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