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Design of cluster-wise optimal fuzzy logic controllers to model input-output relationships of some manufacturing processes

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  • Tushar
  • Dilip Kumar Pratihar

Abstract

In the present study, fuzzy logic (FL)-based approaches have been developed to determine the input-output relationships of some manufacturing processes, which may be non-linear in nature. Moreover, the degree of non-linearity may not be the same over the entire range of the variables. The input-output space has been clustered based on the similarity of the data points and cluster-wise linear regressions have been carried out. Takagi and Sugeno's approach of fuzzy logic controller (FLC) has been implemented cluster-wise using the pre-determined regression equations. A genetic algorithm (GA) has been utilised to optimise both cluster-properties as well as knowledge base of the FLCs developed based on two types of clustering algorithm, namely entropy-based approach and fuzzy C-means algorithm. The performances of above two FLCs have been compared in the present work.

Suggested Citation

  • Tushar & Dilip Kumar Pratihar, 2009. "Design of cluster-wise optimal fuzzy logic controllers to model input-output relationships of some manufacturing processes," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 1(2), pages 178-205.
    • Handle: RePEc:ids:ijdmmm:v:1:y:2009:i:2:p:178-205
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