Design of cluster-wise optimal fuzzy logic controllers to model input-output relationships of some manufacturing processes. (27th May 2009)

• Record Type: Journal Article
• Authors: Pratihar, Dilip Kumar
• Is Part Of: International journal of data mining, modelling and management. Volume 1:Number 2(2009)
• Page Start: 178
• Page End: 205
• Publication Date: 2009-05-27
• Subjects: fuzzy logic controllers -- FLC -- clustering methods -- entropy -- genetic algorithms -- GAs -- manufacturing process -- fuzzy control -- input-output relationships -- optimisation; Data mining -- Periodicals; Information science -- Periodicals; Databases -- Periodicals; 005.7
• Languages: English
• ISSNs: 1759-1163

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.