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Abstract

This study presents an approach for approximation an unknown function y=f(x ̅) from a numerical data set based on a neuro-fuzzy inference system modeling. The focus of interest in proposed approach is to increase degree of accuracy of the degree of this approximation. New algorithms named CSHL, HLM1 and HLM2, which are used for this target, are presented. The first new algorithm, CSHL, which uses functions named pure function ψ and penalty function τ effecting as direction for input data space partition, is used to build data clusters. The second and the third algorithm based on the Hyperplane Clustering algorithm of [1] and the CSHL algorithm are used to establish adaptive neuro-fuzzy inference systems. A series of numerical experiments are performed to assess the efficiency of the proposed approach.



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Article Details

Issue: Vol 11 No 5 (2008)
Page No.: 5-20
Published: May 31, 2008
Section: Engineering and Technology - Research article
DOI: https://doi.org/10.32508/stdj.v11i5.2635

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Creative Commons License

Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Dung, N., & Nhi, N. (2008). BUILDING NEURO-FUZZY INFERENCE SYSTEMS BASED ON INPUTOPTIMAL-FUZZY-SET ESTABLISHMENT. Science and Technology Development Journal, 11(5), 5-20. https://doi.org/https://doi.org/10.32508/stdj.v11i5.2635

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