A new fuzzy approach to improve fashion product development

T. W. Lau, Chi Leung Hui, Frency S.F. Ng, Chun Chung Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

40 Citations (Scopus)

Abstract

This study attempts to use a fuzzy expert system with gradient descent optimization for prediction of fabric specimens in fashion product development. Compared with the traditional methods used fabric mechanical properties to predict fabric specimens, our advisory system accepts fabric hand descriptors which are more closely related to the sensory judgments made by individuals during fabric selection. Fifty participants were selected to evaluate the performance of the proposed fuzzy fabric advisory system. They were asked to express their preferred fabric specimen on inputs of the 14 bipolar fabric hand descriptors in the system. The fuzzy prediction rules associated with the membership functions of each fabric specimen were developed from a survey. After fine-tuning of the proposed system, the prediction accuracy is over eighty percent. The outcomes of this study could help consumers to select the most appropriate fabric and provide field practitioners appropriate suggestions for effective product development in clothing and fashion industries.
Original languageEnglish
Pages (from-to)82-92
Number of pages11
JournalComputers in Industry
Volume57
Issue number1
DOIs
Publication statusPublished - 1 Jan 2006

Keywords

  • Fabric hand descriptors
  • Fabric specimen prediction
  • Fuzzy system
  • Sensory knowledge

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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