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A Novel Parameter Learning Algorithm for a Self-constructing Fuzzy Neural Network Design

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3 Author(s)
Yuan Yao ; Sch. of Comput., Northwestern Polytech. Univ., Xian, China ; Kai-Long Zhang ; Xin-She Zhou

This paper proposes a novel parameter learning algorithm for a self-constructing fuzzy neural network (SCFFN) design. It concludes dynamic prior adjustment (DPA) which is employed to adjust parameters according to the distribution of the input samples and group-based symbiotic evolution (GSE) which is applied to train all the free parameters for the desired outputs. DPA considers the relevance between input samples space and the IF-part parameters, which intends to accomplish coarse adjustment. Then, GSE is adopted to search the global optimum solution. Unlike traditional GA with each gene representing a whole fuzzy system, GSE divides the population into several groups that each one only represents a fuzzy rule. The full solutions can be generated by all possible combinations of the groups. The simulations results have verified that the proposed algorithm achieves superior performance in learning accuracy.

Published in:

Computer and Information Science, 2009. ICIS 2009. Eighth IEEE/ACIS International Conference on

Date of Conference:

1-3 June 2009