A Design of Genetically Optimized Linguistic Models

Keun-Chang KWAK  

IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.12   pp.3117-3120
Publication Date: 2012/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.3117
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Biocybernetics, Neurocomputing
linguistic model,  context-based fuzzy c-means clustering,  genetic algorithm,  coagulant dosing process,  

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In this paper, we propose a method for designing genetically optimized Linguistic Models (LM) with the aid of fuzzy granulation. The fundamental idea of LM introduced by Pedrycz is followed and their design framework based on Genetic Algorithm (GA) is enhanced. A LM is designed by the use of information granulation realized via Context-based Fuzzy C-Means (CFCM) clustering. This clustering technique builds information granules represented as a fuzzy set. However, it is difficult to optimize the number of linguistic contexts, the number of clusters generated by each context, and the weighting exponent. Thus, we perform simultaneous optimization of design parameters linking information granules in the input and output spaces based on GA. Experiments on the coagulant dosing process in a water purification plant reveal that the proposed method shows better performance than the previous works and LM itself.