Fuzzy system identification by generating and evolutionary optimizing fuzzy rule bases consisting of relevant fuzzy rules

dc.contributor.authorKrause, P.de
dc.contributor.authorKrone, A.de
dc.contributor.authorSlawinski, T.de
dc.date.accessioned2004-12-07T08:20:20Z
dc.date.available2004-12-07T08:20:20Z
dc.date.created2000de
dc.date.issued2001-10-16de
dc.description.abstractOne approach forsystem identification among many othersis the fuzzy identification approach. The advantage of this approach compared to other analytical approaches is, that it is not necessary to make an assumption for the model to be used for the identification. In addition, the fuzzy approach can handle nonlinearities easier than analytical approaches. The Fuzzy-ROSA method is a method for data-based generation of fuzzy rules. This is the first step of a two step identification process. The second step is the optimization of the remaining free parameters, i.e. the composition of the rule base and the linguistic terms, to further improve the quality of the model and obtain small interpretable rule bases. In this paper, a new evolutionary strategy for the optimization of the linguistic terms of the output variable is presented. The effectiveness of the two step fuzzy identification is demonstrated on the benchmark problem 'kin dataset' of the Delve dataset repository and the results are compared to analytical and neural network approaches.en
dc.format.extent200754 bytes
dc.format.extent245694 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/5387
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15264
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesReihe Computational Intelligence ; 84de
dc.subjectdata-based fuzzy rule generationen
dc.subjectdelve benchmarken
dc.subjectevolutionary strategyen
dc.subjectfuzzy identificationen
dc.subjectfuzzy system optimizationen
dc.subject.ddc004de
dc.titleFuzzy system identification by generating and evolutionary optimizing fuzzy rule bases consisting of relevant fuzzy rulesen
dc.typeTextde
dc.type.publicationtypereport
dcterms.accessRightsopen access

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