How the parameter-setting affects the inferential capability of artificial neural network -with the corresponding relationship between products form and Kansei images as example

Chun-Juei Chou, Kuohsiang Chen

Research output: Contribution to journalArticle

Abstract

Artificial Neural Network (ANN) has been considered as a better model for inference in the field of Kansei Engineering research. However, the setting of parameters might strongly affect its inferential capability. For this reason, the authors researched on how the parameter-setting in ANN affected its inferential capability, and took the corresponding relationship between products form and Kansei images as example. The parameters mentioned above include three types of coding for nominal scale, four different numbers of hidden units, ten different learning rates and two opposite input and output settings. The results indicated that the coding type for nominal scale set to "on/off" type, number of hidden units set to the product of that of input units and output units, learning rate set to 1.0, formal elements (nominal scale) as input and Kansei images (rank scale) as output could achieve better analytical and inferential outcome.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalJournal of the Chinese Institute of Industrial Engineers
Volume19
Issue number6
DOIs
Publication statusPublished - 2002 Jan 1

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Neural networks
Engineering research

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Cite this

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abstract = "Artificial Neural Network (ANN) has been considered as a better model for inference in the field of Kansei Engineering research. However, the setting of parameters might strongly affect its inferential capability. For this reason, the authors researched on how the parameter-setting in ANN affected its inferential capability, and took the corresponding relationship between products form and Kansei images as example. The parameters mentioned above include three types of coding for nominal scale, four different numbers of hidden units, ten different learning rates and two opposite input and output settings. The results indicated that the coding type for nominal scale set to {"}on/off{"} type, number of hidden units set to the product of that of input units and output units, learning rate set to 1.0, formal elements (nominal scale) as input and Kansei images (rank scale) as output could achieve better analytical and inferential outcome.",
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