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The Taguchi System-Two Steps Optimal Algorithm Based Neural Network for Dynamic Sensor Product Design

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Frontier Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 375))

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Abstract

The key successful factor of the new product design (NPD) of sensor product industry is the selections of the best parameter level. General speaking, decreasing the error rate of product parameter selection by increasing the number of experiments is the commodity trend in NPD goals. For above reasons, previous studies focus on structured approach for the replacement and management of selection of the parameter level in product design with the purpose of increasing efficiency and effectiveness, but rarely on a dynamic environment. Consequently, this work presents a novel algorithm, the Taguchi System-two steps optimal algorithm, which combines the Taguchi System (TS) with two steps optimal (TSO) method, which is shown how product adjusted under a dynamic environment in product design. The utility of the parameter level are selected. The two step optimal (TSO) method links the decisions for selections of parameter level in two different times and can be used to focus on dynamic sensor product design system (DSPDS). From the results, the proposed method might possibly be useful for our problem by selecting the parameter level size and adjusting the parameters by TSO and neural network (NN) in the DSPDS is observed in this study.

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Author information

Authors and Affiliations

  1. Department of Industrial Management, Lunghwa University of Science and Technology, 300, Sec. 1, Wanshou Rd, Guishan, Taoyuan, 33306, Taiwan

    Ching-Lien Huang

  2. Department of Computer Information and Network Engineering, LungHwa University of Science and Technology, Taoyuan, Taiwan

    Yung-Hui Chen

  3. Department of Information Management, Nanhua University, Chia-Yi, 62249, Taiwan

    Chun-Hsiung Tseng

  4. Department of Information Management, Lunghwa University of Science and Technology, Taoyuan, Taiwan

    Tian-Long John Wan

  5. Department of Component BG, Lunghwa University of Science and Technology, Taoyuan, Taiwan

    Lung-Cheng Wang

  6. Department of Business Administration, Fu Jen Catholic University, New Taipei, Taiwan

    Chang-Lin Yang

Authors
  1. Ching-Lien Huang
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  2. Yung-Hui Chen
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  3. Chun-Hsiung Tseng
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  4. Tian-Long John Wan
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  5. Lung-Cheng Wang
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  6. Chang-Lin Yang
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Corresponding author

Correspondence to Chun-Hsiung Tseng .

Editor information

Editors and Affiliations

  1. Dept of Information Tech, Overseas Chinese University, Taichung, Taiwan

    Jason C Hung

  2. Aizu-Wakamatsu, Fukushima, Japan

    Neil Y. Yen

  3. Providence University, Taichung, Taiwan

    Kuan-Ching Li

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© 2016 Springer Science+Business Media Singapore

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Huang, CL., Chen, YH., Tseng, CH., Wan, TL.J., Wang, LC., Yang, CL. (2016). The Taguchi System-Two Steps Optimal Algorithm Based Neural Network for Dynamic Sensor Product Design. In: Hung, J., Yen, N., Li, KC. (eds) Frontier Computing. Lecture Notes in Electrical Engineering, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-10-0539-8_54

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  • DOI: https://doi.org/10.1007/978-981-10-0539-8_54

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0538-1

  • Online ISBN: 978-981-10-0539-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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