Dynamic neural fuzzy inference system

Authors:
Yuan-Chun Hwang;Qun Song
Affiliations:
Auckland University of Technology, Knowledge Engineering and Discovery Research Institute, Auckland, New Zealand;Auckland University of Technology, Knowledge Engineering and Discovery Research Institute, Auckland, New Zealand
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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Abstract

This paper proposes an extension to the original offline version of DENFIS. The new algorithm, DyNFIS, replaces original triangular membership function with Gaussian membership function and use back-propagation to further optimizes the model. Fuzzy rules are created for each clustering centre based on the clustering outcome of evolving clustering method. For each test data, the output of DyNFIS is calculated through fuzzy inference system based on m-most activated fuzzy rules and these rules are updated based on back-propagation to minimize the error. DyNFIS shows improvement on multiple benchmark data and satisfactory result in NN3 forecast competition.