Design rule extraction from a trained ann model using ga for product form design of mobile phones

K. Y. Fung, Chak Yin Tang, Eric W.M. Lee, G. T.S. Ho, Michael K.W. Siu, W. L. Mou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

An artificial neural network (ANN) model and rule extraction from a trained ANN using genetic algorithm (GA) are applied to predict and advise on the rules for optimal product form design for a particular customer feeling. To map design elements and the affected impressions, principal component analysis (PCA) is employed to determine the essential dimensions for data analysis. By using ANN to examine the relationships between perceptual value and form elements, black-box ANN knowledge can be extracted by applying GA to generate design rules. A case study on the product form of a mobile phone design was conducted to implement the proposed approach. The resultant rules can be used to help product designers to better understand key design elements and to verify optimal solutions suggested by using ANN models.
Original languageEnglish
Pages (from-to)369-379
Number of pages11
JournalIntelligent Automation and Soft Computing
Volume18
Issue number4
DOIs
Publication statusPublished - 1 Jan 2012

Keywords

  • Affective design
  • Artificial neural network (ANN)
  • Product form design
  • Rule extraction from ANN

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

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