Publikation: Application of Fuzzy Graphs for Metamodeling
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1998
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Huber, Klaus-Peter
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1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228). IEEE, 1998, pp. 640-644. ISBN 0-7803-4863-X. Available under: doi: 10.1109/FUZZY.1998.687563
Zusammenfassung
In system design and system engineering one important aspect is the construction of a (simulation) model, which behaves like the system under consideration, but saves costs and time. However, analysis of this model is often a complex and difficult task due to the underlying nonlinear relations. One solution is to build a so-called metamodel from the model, which is easier to analyze because of its focus on the input/output behaviour instead of the underlying structure. A promising approach for this metamodelling task is the usage of fuzzy graphs which allow one to understand the metamodel and approximation of the model function is possible. To show its usefulness in practice the metamodelling results for a complex real world model of a communication system are given
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1998 IEEE International Conference on Fuzzy Systems IEEE World Congress on Computational Intelligence, Anchorage, AK, USA
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HUBER, Klaus-Peter, Michael R. BERTHOLD, 1998. Application of Fuzzy Graphs for Metamodeling. 1998 IEEE International Conference on Fuzzy Systems IEEE World Congress on Computational Intelligence. Anchorage, AK, USA. In: 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228). IEEE, 1998, pp. 640-644. ISBN 0-7803-4863-X. Available under: doi: 10.1109/FUZZY.1998.687563BibTex
@inproceedings{Huber1998Appli-24288,
year={1998},
doi={10.1109/FUZZY.1998.687563},
title={Application of Fuzzy Graphs for Metamodeling},
isbn={0-7803-4863-X},
publisher={IEEE},
booktitle={1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228)},
pages={640--644},
author={Huber, Klaus-Peter and Berthold, Michael R.}
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<dcterms:abstract xml:lang="eng">In system design and system engineering one important aspect is the construction of a (simulation) model, which behaves like the system under consideration, but saves costs and time. However, analysis of this model is often a complex and difficult task due to the underlying nonlinear relations. One solution is to build a so-called metamodel from the model, which is easier to analyze because of its focus on the input/output behaviour instead of the underlying structure. A promising approach for this metamodelling task is the usage of fuzzy graphs which allow one to understand the metamodel and approximation of the model function is possible. To show its usefulness in practice the metamodelling results for a complex real world model of a communication system are given</dcterms:abstract>
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