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Conference Papers Year : 2010

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

Abdullah Almaksour
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Eric Anquetil

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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Dates and versions

hal-00763296 , version 1 (10-12-2012)

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  • HAL Id : hal-00763296 , version 1

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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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