Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Krause, P. | de |
dc.contributor.author | Krone, A. | de |
dc.contributor.author | Lindenblatt, M. | de |
dc.contributor.author | Slawinski, T. | de |
dc.date.accessioned | 2004-12-07T08:19:51Z | - |
dc.date.available | 2004-12-07T08:19:51Z | - |
dc.date.created | 1998 | de |
dc.date.issued | 2001-10-16 | de |
dc.identifier.uri | http://hdl.handle.net/2003/5360 | - |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-15267 | - |
dc.description.abstract | In this paper we propose a hybrid fuzzy-evolutionary system for fuzzy modelling in high dimensional spaces. The system architecture is based on a Michigan-style approach (one individual represents one fuzzy rule). The design of the evolutionary algorithm makes use of a distance measure in the search space that in turn reflects some heuristic assumptions about the fitness landscape. Additionally, strategy parameters are dynamically adapted by means of a fuzzy controller. The approach is successfully applied to a complex benchmark problem as well as to several real-world modelling tasks such as the cancellation behaviour of insurance clients and the classification of automatic gearboxes. | en |
dc.format.extent | 2055108 bytes | - |
dc.format.extent | 686715 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.format.mimetype | application/postscript | - |
dc.language.iso | en | de |
dc.publisher | Universität Dortmund | de |
dc.relation.ispartofseries | Reihe Computational Intelligence ; 50 | de |
dc.subject.ddc | 004 | de |
dc.title | A Hybrid Evolutionary Search Concept for Data-based Generation of Relevant Fuzzy Rules in High Dimensional Spaces | en |
dc.type | Text | de |
dc.type.publicationtype | report | - |
dcterms.accessRights | open access | - |
Appears in Collections: | Sonderforschungsbereich (SFB) 531 |
Files in This Item:
File | Description | Size | Format | |
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ci50.pdf | DNB | 670.62 kB | Adobe PDF | View/Open |
ci50.ps | 2.01 MB | Postscript | View/Open |
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