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Journal Articles Evolving Systems Year : 2011

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

Abstract

We present in this paper a new method for the design of evolving neurofuzzy 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-00741483 , version 1 (12-10-2012)

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Abdullah Almaksour, Eric Anquetil. Improving Premise Structure in Evolving Takagi-Sugeno Neuro-Fuzzy Classifiers. Evolving Systems, 2011, 2 (1), pp.25-33. ⟨10.1007/s12530-011-9027-0⟩. ⟨hal-00741483⟩
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