Applications of soft computing techniques in response surface based approximate optimization

Jongsoo Lee, Seungjin Kim

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Two different approximation methods referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the training data is not sufficiently provided or uncertain information may be included in design process. Fuzzy inference system is the central system for of identifying the input/output relationship in both methods. The paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and presents their generalization capabilities in terms of a number of fuzzy rules and training data with application to a three-bar truss optimization.

Original languageEnglish
Pages (from-to)1132-1142
Number of pages11
JournalKSME International Journal
Volume15
Issue number8
DOIs
Publication statusPublished - 2001 Jan 1

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Soft computing
Fuzzy rules
Fuzzy inference
Fuzzy systems
Membership functions
Genetic algorithms
Neural networks
Design optimization

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering

Cite this

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Applications of soft computing techniques in response surface based approximate optimization. / Lee, Jongsoo; Kim, Seungjin.

In: KSME International Journal, Vol. 15, No. 8, 01.01.2001, p. 1132-1142.

Research output: Contribution to journalArticle

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