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

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...

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Bibliographic Details
Published in2010 International Conference on Machine Learning and Applications pp. 586 - 591
Main Authors Almaksour, A, Anquetil, E
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Adaptation model
Clustering algorithms
Covariance matrix
Incremental learning
neuro-fuzzy
Prototypes
Takagi-Sugeno
Takagi-Sugeno model
Tuning
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ISBN1424492114
9781424492114
DOI10.1109/ICMLA.2010.91

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Summary: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.
ISBN:1424492114
9781424492114
DOI:10.1109/ICMLA.2010.91