Simulation Data Analysis Using Fuzzy Graphs

Cite This

Files in this item

Checksum: MD5:4f7db7ae86bab41d1d279360e5d46ba3

HUBER, Klaus-Peter, Michael R. BERTHOLD, 2006. Simulation Data Analysis Using Fuzzy Graphs. In: LIU, Xiaohui, ed., Paul COHEN, ed., Michael BERTHOLD, ed.. Advances in Intelligent Data Analysis Reasoning about Data. Berlin, Heidelberg:Springer Berlin Heidelberg, pp. 347-358. ISBN 978-3-540-63346-4. Available under: doi: 10.1007/BFb0052853

@inproceedings{Huber2006-05-19Simul-24079, title={Simulation Data Analysis Using Fuzzy Graphs}, year={2006}, doi={10.1007/BFb0052853}, number={1280}, isbn={978-3-540-63346-4}, address={Berlin, Heidelberg}, publisher={Springer Berlin Heidelberg}, series={Lecture Notes in Computer Science}, booktitle={Advances in Intelligent Data Analysis Reasoning about Data}, pages={347--358}, editor={Liu, Xiaohui and Cohen, Paul and Berthold, Michael}, author={Huber, Klaus-Peter and Berthold, Michael R.} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dc:rights>terms-of-use</dc:rights> <dcterms:available rdf:datatype="">2013-07-15T07:31:32Z</dcterms:available> <dspace:hasBitstream rdf:resource=""/> <dc:contributor>Berthold, Michael R.</dc:contributor> <dcterms:issued>2006-05-19</dcterms:issued> <dc:language>eng</dc:language> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dspace:isPartOfCollection rdf:resource=""/> <bibo:uri rdf:resource=""/> <dcterms:abstract xml:lang="eng">Analysis of simulation models has gained considerable interest in the past. However, their complexity still remains a considerable drawback in practical applications. A promising concept is to analyze the data from simulation experiments. Existing approaches are either restricted to simple models or are hard to interpret. We present an efficient algorithm that constructs a fuzzy graph model from simulation data and we show that the resulting system approximates also complex model functions with an adjustable precision. In addition the Fuzzy Graph allows the analyst to directly access easy to interpret if-then-rules. These rules help to understand the original simulation model, which is shown with a real world token bus model.</dcterms:abstract> <dcterms:hasPart rdf:resource=""/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:bibliographicCitation>Advances in intelligent data analysis : reasoning about data; second international symposium, IDA-97, London, UK, August 4-6, 1997; proceedings / Xiaohui Liu ... (ed.). - Berlin [u.a.] : Springer, 1997. - S. 347-358. - (Lecture notes in computer science ; 1280). - ISBN 3-540-63346-4</dcterms:bibliographicCitation> <dc:contributor>Huber, Klaus-Peter</dc:contributor> <dcterms:isPartOf rdf:resource=""/> <dc:creator>Huber, Klaus-Peter</dc:creator> <dc:creator>Berthold, Michael R.</dc:creator> <dc:date rdf:datatype="">2013-07-15T07:31:32Z</dc:date> <dcterms:rights rdf:resource=""/> <dcterms:title>Simulation Data Analysis Using Fuzzy Graphs</dcterms:title> </rdf:Description> </rdf:RDF>

Downloads since Oct 1, 2014 (Information about access statistics)

Huber_240797.pdf 1124

This item appears in the following Collection(s)

Search KOPS


My Account