A Hybrid Evolutionary Search Concept for Data-based Generation of Relevant Fuzzy Rules in High Dimensional Spaces
| 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.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.identifier.uri | http://hdl.handle.net/2003/5360 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-15267 | |
| 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 |