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System modeling with fuzzy plug‐ins

Witold Pedrycz (Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland, and)
George Vukovich (Canadian Space Agency, Spacecraft Engineering, Saint‐Hubert, Quebec, Canada)

Kybernetes

ISSN: 0368-492X

Article publication date: 1 June 2000

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.

Keywords

Citation

Pedrycz, W. and Vukovich, G. (2000), "System modeling with fuzzy plug‐ins", Kybernetes, Vol. 29 No. 4, pp. 473-490. https://doi.org/10.1108/03684920010322226

Publisher

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MCB UP Ltd

Copyright © 2000, MCB UP Limited