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System modeling with fuzzy plug‐ins

System modeling with fuzzy plug‐ins In this study, we introduce and discuss a concept of fuzzy plug‐ins and investigate their role in system modeling. Fuzzy plug‐ins are rule‐based constructs augmenting a given global model (arising in the form of some regression relationship, neural network, etc.) in the sense that they compensate for the mapping errors produced by the global model. The proposed design method develops around information granules of error defined in the output space and the induced fuzzy relations expressed in the space of input variables. The construction of the linguistic granules is carried out with the aid of context‐based fuzzy clustering – a generalized version of the well‐known FCM algorithm that is well‐suited to the design of fuzzy sets and relations being used as a blueprint of the plug‐ins. An overall modeling architecture combining the global model with its plug‐ins is discussed in detail and a complete design procedure is provided. Finally, some illustrative numerical examples are shown as well. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Kybernetes Emerald Publishing

System modeling with fuzzy plug‐ins

Kybernetes , Volume 29 (4): 18 – Jun 1, 2000

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Publisher
Emerald Publishing
Copyright
Copyright © 2000 MCB UP Ltd. All rights reserved.
ISSN
0368-492X
DOI
10.1108/03684920010322226
Publisher site
See Article on Publisher Site

Abstract

In this study, we introduce and discuss a concept of fuzzy plug‐ins and investigate their role in system modeling. Fuzzy plug‐ins are rule‐based constructs augmenting a given global model (arising in the form of some regression relationship, neural network, etc.) in the sense that they compensate for the mapping errors produced by the global model. The proposed design method develops around information granules of error defined in the output space and the induced fuzzy relations expressed in the space of input variables. The construction of the linguistic granules is carried out with the aid of context‐based fuzzy clustering – a generalized version of the well‐known FCM algorithm that is well‐suited to the design of fuzzy sets and relations being used as a blueprint of the plug‐ins. An overall modeling architecture combining the global model with its plug‐ins is discussed in detail and a complete design procedure is provided. Finally, some illustrative numerical examples are shown as well.

Journal

KybernetesEmerald Publishing

Published: Jun 1, 2000

Keywords: Cybernetics; Modelling; Fuzzy sets; Systems

References