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Development of a systematic methodology of fuzzy logic modeling
Department of Mechanical and Industrial Engineering, University of Toronto.ORCID iD: 0000-0003-4977-6339
IEEE, Department of Mechanical and Industrial Engineering, University of Toronto.
IEEE, Department of Mechanical and Industrial Engineering, University of Toronto.
1998 (English)In: IEEE transactions on fuzzy systems, ISSN 1063-6706, E-ISSN 1941-0034, Vol. 6, no 3, p. 346-361Article in journal (Refereed) Published
Abstract [en]

This paper proposs a systematic methodology of fuzzy logic modeling as a generic tool for modeling of complex systems. The methodology conveys three distinct features: 1) a unified parameterized reasoning formulation; 2) an improved fuzzy clustering algorithm; and 3) an efficient strategy of selecting significant system inputs and their membership functions. The reasoning mechanism introduces four parameters whose variation provides a continuous range of inference operation. As a result, we are no longer restricted to standard extremes in any step of reasoning. Unlike traditional approach of selecting the inference mechanism a priori, the fuzzy model itself can then adjust the reasoning process by optimizing the inference parameters based on input-output data. The fuzzy rules are generated through fuzzy c-means (FCM) clustering algorithm. Major bottle-necks of the algorithm are addressed and analytical solutions are suggested. Furthermore, we also address the classification process in fuzzy modelng to extend the derived fuzzy partition to the entire output space. This issue remains unattained in the current literature. In order to select suitable input variables among a finite number of candidates (unlike traditional approaches) we suggest a new strategy through which dominant input parameters are assigned in one step and no iteration process is required. Furthermore, a clustering technique called fuzzy line clustering is introduced to assign the input membership functions. In order to evaluate the proposed methodology, two examples - a nonlinear function and a gas furnace dynamic procedure - are investigated in detail. The significant improvement of the model is concluded compared to other fuzzy modeling approaches. © 1998 IEEE.

Place, publisher, year, edition, pages
1998. Vol. 6, no 3, p. 346-361
Keywords [en]
Approximate reasoning, Fuzzy clustering, Fuzzy modeling, Fuzzy systems
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Onboard space systems
Identifiers
URN: urn:nbn:se:ltu:diva-12757DOI: 10.1109/91.705501ISI: 000075285100005Scopus ID: 2-s2.0-0032138824Local ID: beb69a3e-7bf1-4ee1-a733-544ed745dcfeOAI: oai:DiVA.org:ltu-12757DiVA, id: diva2:985708
Note

Upprättat; 1998; 20141215 (ninhul)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-10-04Bibliographically approved

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