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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Communication dans un congrès
International Conference on Machine Learning and Applications ICMLA, 2010, washington, United States. 2010
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https://hal.inria.fr/hal-00763296
Contributeur : Abdullah Almousa Almaksour <>
Soumis le : lundi 10 décembre 2012 - 14:53:29
Dernière modification le : vendredi 25 mai 2018 - 01:07:47
Document(s) archivé(s) le : lundi 11 mars 2013 - 12:35:55

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AlmaksourICMLA10.pdf
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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. 2010. 〈hal-00763296〉

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