Authors: | Krause, P. Krone, A. Lindenblatt, M. Slawinski, T. |
Title: | A Hybrid Evolutionary Search Concept for Data-based Generation of Relevant Fuzzy Rules in High Dimensional Spaces |
Language (ISO): | en |
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. |
URI: | http://hdl.handle.net/2003/5360 http://dx.doi.org/10.17877/DE290R-15267 |
Issue Date: | 2001-10-16 |
Provenance: | Universität Dortmund |
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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This item is protected by original copyright
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