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Representation and Propagation of Information Granules in Rule-based Computing
Witold Pedrycz* and George Vulcovich**
*Department of Electrical & Computer Engineering, University of Alberta, Edmonton T6G 2G7 Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
**Canadian Space Agency, Spacecraft Engineering 6767 Route de I’Aeroport Saint-Hubert, Quebec J3Y 8Y9 Canada
Received:October 13, 1998Accepted:May 24, 1999Published:January 20, 2000
Keywords:Information granulation and information granules, Rule-based system, Fuzzy sets, Interval analysis, Granularity propagation, Possibility, Necessity, Compatibility, Regression models
Abstract
The study is devoted to the paradigm of rule based computing involving granular information. By information granules we mean a general category of data embracing not only numeric entities (inputs) but any granules (such as intervals or fuzzy sets, in general) being regarded as inputs in the rule-based system. We investigate several categories of models of granularity propagation starting from those based on the use of the mechanisms of possibility and necessity theory, especially possibility and possibility-necessity mechanisms. We also consider the models relying on the use of auxiliary regression models. These models are constructed on the basis of some experimental granular data. A thorough comparative analysis of the introduced models is carried out as well.
Cite this article as:W. Pedrycz and G. Vulcovich, “Representation and Propagation of Information Granules in Rule-based Computing,” J. Adv. Comput. Intell. Intell. Inform., Vol.4 No.1, pp. 102-110, 2000.Data files:
