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Improving Premise Structure in Evolving Takagi-Sugeno Neuro-Fuzzy Classifiers

Abstract : We present in this paper a new method for the design of evolving neuro-fuzzy classifiers. The presented approach is based on a first-order Takagi-Sugeno neuro-fuzzy model. We propose a modification on the premise structure in this model and we provide the necessary learning formulas, with no problem-dependent parameters. We demonstrate by the experimental results the positive effect of this modification on the overall classification performance
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Contributor : Abdullah Almousa Almaksour <>
Submitted on : Monday, December 10, 2012 - 2:53:29 PM
Last modification on : Thursday, January 7, 2021 - 4:11:24 PM
Long-term archiving on: : Monday, March 11, 2013 - 12:35:55 PM


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  • HAL Id : hal-00763296, version 1


Abdullah Almaksour, Eric Anquetil. Improving Premise Structure in Evolving Takagi-Sugeno Neuro-Fuzzy Classifiers. International Conference on Machine Learning and Applications ICMLA, 2010, washington, United States. ⟨hal-00763296⟩



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