Industrial applications of soft computing: a review

Fuzzy logic, neural networks, and evolutionary computation are the core methodologies of soft computing (SC). SC is causing a paradigm shift in engineering and science fields since it can solve problems that have not been able to be solved by traditional analytic methods. In addition, SC yields rich...

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Bibliographic Details
Published inProceedings of the IEEE Vol. 89; no. 9; pp. 1243 - 1265
Main Authors Dote, Y., Ovaska, S.J.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.09.2001
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Analogies
Computer applications
Computer industry
Computer networks
Construction industry
Costs
Evolutionary computation
Fuzzy logic
Intelligent systems
Knowledge acquisition
Knowledge representation
Low cost
Mathematical analysis
Neural networks
Soft computing
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Summary:Fuzzy logic, neural networks, and evolutionary computation are the core methodologies of soft computing (SC). SC is causing a paradigm shift in engineering and science fields since it can solve problems that have not been able to be solved by traditional analytic methods. In addition, SC yields rich knowledge representation, flexible knowledge acquisition, and flexible knowledge processing, which enable intelligent systems to be constructed at low cost. This paper reviews applications of SC in several industrial fields to show the various innovations by TR, HMIQ, and low cost in industries that have been made possible by the use of SC. Our paper intends to remove the gap between theory and practice and attempts to learn how to apply soft computing practically to industrial systems from examples/analogy, reviewing many application papers.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0018-9219
1558-2256
DOI:10.1109/5.949483