International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Chi-Bin Cheng*

Department of Industrial Engineering and Management, Chaoyang University of Technology, Wufeng, Taichung country 413, Taiwan, R.O.C.

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Modeling and optimization of a process with multiple outputs is discussed in this paper. A neuro-fuzzy system named MANFIS, which comprises a fuzzy inference structure and neural network learning ability, is used to model a multiple output process. Optimization of such a process is formulated as a multiple objective decision making problem, and a genetic algorithm and a numerical method are introduced, respectively, to solve this problem based on the MANFIS model. We have used these two algorithms, respectively, to solve a chemical process optimization problem, and compared their performances. A combination of these two algorithms is also suggested to improve performances of both algorithms. The proposed approach is also applied to a wire-bonding problem in semiconductor manufacturing.

Keywords: process optimization; soft computing; neuro-fuzzy system; genetic algorithm, multi-ple objective decision making; wire bonding.

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Accepted: 2003-12-23
Available Online: 2004-03-02

Cite this article:

Cheng, C.-B., 2004. Process optimization by soft computing and its application to a wire bonding problem, International Journal of Applied Science and Engineering, 2, 59–71.

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