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


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Article Info

DATE

2005-10

AUTHORS

Chao-Ton Su, Mu-Chen Chen, Hsiao-Ling Chan

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. More... »

PAGES

1132-1140

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1057/palgrave.jors.2601888

DOI

http://dx.doi.org/10.1057/palgrave.jors.2601888

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017711318


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