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An Algorithm for Clustering Input Variables in a Fuzzy Model in a FLC Process
International Journal of Management and Fuzzy Systems
Volume 6, Issue 2, June 2020, Pages: 29-46
Received: Sep. 18, 2020; Accepted: Oct. 6, 2020; Published: Oct. 13, 2020
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Nenad Stojanovic, Faculty of Agriculture, University of Banja Luka, Banja Luka, Bosnia and Herzegovina
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The input and output variables in fuzzy systems are linguistic variables. The base of the fuzzy rule represents the central part of a fuzzy controller, and the fuzzy rule represents its basic part, and it has the following form: "if R then P", where R and P represent the fuzzy relation, i.e. the proposition. Complex systems described by fuzzy relations generate a large number of inference rules. Grouping the states into clusters on the basis of which we make conclusions about the value of the output variable is performed by an expert based on his or her experience and knowledge. Ideally, the number of clusters should correspond to the number of attributes by which the value of the output variable is classified, which, in reality is not the case. In the absence of experts, we perform grouping on the basis of some of the criteria. One way of grouping descriptive states into clusters is presented in this paper. It presents a construction of the method of grouping descriptive states of fuzzy models, with the aim of drawing conclusions about the value of the output variable described by a given state. The presented method of grouping descriptive states is based on defined characteristic values associated with fuzzy numbers by which the input variables of the model are evaluated. They represent the basis for defining the characteristic value of the descriptive state of the output variable of a fuzzy model. For the presented method, a mathematical logical argumentation of the application is given, as an algorithm for the application of the constructed method. The application of the algorithm is demonstrated in measuring the economic dimension of the sustainability of tourism development, measured by comparative evaluation indicators.
Data Clustering, Reduction of Inference Rules, Algorithms, Mathematical Modeling, FLC Processes
To cite this article
Nenad Stojanovic, An Algorithm for Clustering Input Variables in a Fuzzy Model in a FLC Process, International Journal of Management and Fuzzy Systems. Vol. 6, No. 2, 2020, pp. 29-46. doi: 10.11648/j.ijmfs.20200602.12
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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