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- W2082054095 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." @default.
- W2082054095 created "2016-06-24" @default.
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- W2082054095 date "2009-01-01" @default.
- W2082054095 modified "2023-09-26" @default.
- W2082054095 title "Design of cluster-wise optimal fuzzy logic controllers to model input-output relationships of some manufacturing processes" @default.
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- W2082054095 doi "https://doi.org/10.1504/ijdmmm.2009.026075" @default.
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