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- W2966785512 abstract "Many Industrial processes have multiple quality characteristics to be optimized. However, among them some are conflicting with each other and optimizing one response causes loss in the other response. Such a problem is called Multiresponse optimization (MRO) problem which involves optimizing conflicting multiple responses simultaneously. Unifying approaches are the most common approaches to solve MRO problem, theses approaches convert a MRO problem into a single response optimization problem by combining the multiple responses into one index, which is used for optimization. Principal Component Analysis (PCA) is one of the tool used to unify the responses. The major advantage of PCA is it takes consideration of correlation among responses. However, there are certain shortcomings as well, this paper address those shortcomings and proposes an Weighted PCA- Artificial Neural Network- Genetic Algorithm (WPCA-ANN-GA ) approach to perform multi response optimization. A case study using data from published literature is presented at the end to demonstrate the WPCA-ANN-GA approach." @default.
- W2966785512 created "2019-08-13" @default.
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- W2966785512 date "2018-12-01" @default.
- W2966785512 modified "2023-09-25" @default.
- W2966785512 title "An Integrated WPCA-ANN-GA Approach to Solve Multiresponse Optimization Problems" @default.
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- W2966785512 doi "https://doi.org/10.1109/ctems.2018.8769303" @default.
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