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Data-Based System Modeling Using a Type-2 Fuzzy Neural Network With a Hybrid Learning Algorithm

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3 Author(s)
Chi-Yuan Yeh ; Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan ; Jeng, W.R. ; Shie-Jue Lee

We propose a novel approach for building a type-2 neural-fuzzy system from a given set of input-output training data. A self-constructing fuzzy clustering method is used to partition the training dataset into clusters through input-similarity and output-similarity tests. The membership function associated with each cluster is defined with the mean and deviation of the data points included in the cluster. Then a type-2 fuzzy Takagi-Sugeno-Kang IF-THEN rule is derived from each cluster to form a fuzzy rule base. A fuzzy neural network is constructed accordingly and the associated parameters are refined by a hybrid learning algorithm which incorporates particle swarm optimization and a least squares estimation. For a new input, a corresponding crisp output of the system is obtained by combining the inferred results of all the rules into a type-2 fuzzy set, which is then defuzzified by applying a refined type reduction algorithm. Experimental results are presented to demonstrate the effectiveness of our proposed approach.

Published in:

Neural Networks, IEEE Transactions on  (Volume:22 ,  Issue: 12 )

Date of Publication:

Dec. 2011

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