Machine learning in engineering design-an unsupervised fuzzy neural network case-based learning model

Shih-Lin Hung, J. C. Jan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Engineering design is a creative and experience oriented process. Facing a new design case, an experienced designer will recall the similar cases in a case base which have been solved before. Then, the designer will attempt to find the solution from these similar cases in a way of adaptation or synthesis. An unsupervised fuzzy neural network (UFN) case-based learning model has been developed to perform the aforementioned design process and implemented in two steps. The UFN learning model has been applied to the domain of engineering design. The learning results show that the learning performance of the new learning model is superior to that of a supervised learning model only in complicated or discrete domains. Also, the unsupervised fuzzy neural network learning model can learn complicated design problems within a reasonable CPU time.

Original languageEnglish
Title of host publicationProceedings - Intelligent Information Systems, IIS 1997
EditorsHojjat Adeli
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages156-160
Number of pages5
ISBN (Electronic)0818682183, 9780818682186
DOIs
StatePublished - 1 Jan 1997
Event1997 International Conference on Intelligent Information Systems, IIS 1997 - Grand Bahama Island, Bahamas
Duration: 8 Dec 199710 Dec 1997

Publication series

NameProceedings - Intelligent Information Systems, IIS 1997

Conference

Conference1997 International Conference on Intelligent Information Systems, IIS 1997
CountryBahamas
CityGrand Bahama Island
Period8/12/9710/12/97

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    Hung, S-L., & Jan, J. C. (1997). Machine learning in engineering design-an unsupervised fuzzy neural network case-based learning model. In H. Adeli (Ed.), Proceedings - Intelligent Information Systems, IIS 1997 (pp. 156-160). [645209] (Proceedings - Intelligent Information Systems, IIS 1997). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIS.1997.645209