A neurofuzzy-evolutionary approach for product design

Shih-Wen Hsiao, Elim Liu

Research output: Contribution to journalArticle

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)323-338
Number of pages16
JournalIntegrated Computer-Aided Engineering
Volume11
Issue number4
Publication statusPublished - 2004

Fingerprint

Morphological Analysis
Fuzzy Theory
Neuro-fuzzy
Back-propagation Neural Network
Product Design
Backpropagation
Product design
Artificial intelligence
Artificial Intelligence
Genetic algorithms
Genetic Algorithm
Neural networks
Network Algorithms
Parameter Design
Customers
Optimal Solution
Optimise
Synthesis
Methodology
Evaluate

All Science Journal Classification (ASJC) codes

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

Cite this

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A neurofuzzy-evolutionary approach for product design. / Hsiao, Shih-Wen; Liu, Elim.

In: Integrated Computer-Aided Engineering, Vol. 11, No. 4, 2004, p. 323-338.

Research output: Contribution to journalArticle

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