Applying neural network and scatter search to optimize parameter design with dynamic characteristics

Chao-Ton Su, Mu-Chen Chen () and Hsiao-Ling Chan
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Chao-Ton Su: National Tsing Hua University
Mu-Chen Chen: National Taipei University of Technology
Hsiao-Ling Chan: Ta Hwa Institute of Technology

Journal of the Operational Research Society, 2005, vol. 56, issue 10, 1132-1140

Abstract: Abstract Parameter design is critical to enhancing a system's robustness by identifying specific control factor set points (levels) that make the system least sensitive to noise. Engineers have conventionally applied Taguchi methods to optimize parameter design. However, Taguchi methods can only obtain the optimal solution among the specified control factor levels. They cannot identify the real optimum when the parameter values are continuous. This study proposes a hybrid procedure combining neural networks and scatter search to optimize the continuous parameter design problem. First, neural networks are used to simulate the relationship between the control factor values and corresponding responses. Second, scatter search is employed to obtain the optimal parameter settings. The desirability function is utilized to transform the multiple responses into a single response. A case with dynamic characteristics is carried out in blood glucose strip manufacturing in Taiwan to demonstrate the practicability of the proposed procedure.

Keywords: parameter design; dynamic characteristic; multi-response; neural network; scatter search (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (3)

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DOI: 10.1057/palgrave.jors.2601888

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