A neurofuzzy-evolutionary approach for product design

Authors:
Shih-Wen Hsiao;Elim Liu
Affiliations:
Dept. of Industrial Design, National Cheng Kung Univ., Tainan 70101, Taiwan (Correspd. Tel.: +1 886 6 2757575 ext 54330/ Fax: +1 886 6 2746088/ E-mail: swhsiao@mail.ncku.edu.tw, swhsiao2002@yahoo. ...;Department of Industrial Design, National Cheng Kung University, Tainan 70101, Taiwan
Venue:
Integrated Computer-Aided Engineering
Year:
2004

Citing 0
Cited 3

A hybrid Kansei engineering design expert system based on grey system theory and support vector regression

Expert Systems with Applications: An International Journal
Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design

Computers and Industrial Engineering
A classification-based Kansei engineering system for modeling consumers' affective responses and analyzing product form features

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This study investigates a systematic approach to product design based on artificial intelligence. This investigation proposes the use of artificial intelligence techniques, including fuzzy theory, back propagation neural networks (BPN), and genetic algorithms (GA), along with morphological analysis to synthesize, evaluate and optimize product design. This study focuses on (1) how to model imprecise market information by applying fuzzy theory; (2) mapping relationships between design parameters and customer requirements using BPN; (3) synthesizing design alternatives by morphological analysis, and (4) realizing the synthesis in GA, using its searching capacity to obtain the optimal solution. Two case studies illustrate the practical value of the proposed methodology.