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

Tushar and Dilip Kumar Pratihar

International Journal of Data Mining, Modelling and Management, 2009, vol. 1, issue 2, 178-205

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.

Keywords: fuzzy logic controllers; FLC; clustering methods; entropy; genetic algorithms; GAs; manufacturing process; fuzzy control; input-output relationships; optimisation. (search for similar items in EconPapers)
Date: 2009
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