Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Identification of Fuzzy Inference Systems Using a Multi-objective Space Search Algorithm and Information Granulation

  • Huang, Wei (School of Computer and Communication Engineering, Tianjin University of Technology) ;
  • Oh, Sung-Kwun (Dept. of Electrical Engineering, The University of Suwon) ;
  • Ding, Lixin (State Key Laboratory of Software Engineering, Wuhan University) ;
  • Kim, Hyun-Ki (Dept. of Electrical Engineering, The University of Suwon) ;
  • Joo, Su-Chong (Dept. of Computer Engineering, Wonkwang University)
  • Received : 2010.09.07
  • Accepted : 2011.03.11
  • Published : 2011.11.01
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Abstract

We propose a multi-objective space search algorithm (MSSA) and introduce the identification of fuzzy inference systems based on the MSSA and information granulation (IG). The MSSA is a multi-objective optimization algorithm whose search method is associated with the analysis of the solution space. The multi-objective mechanism of MSSA is realized using a non-dominated sorting-based multi-objective strategy. In the identification of the fuzzy inference system, the MSSA is exploited to carry out parametric optimization of the fuzzy model and to achieve its structural optimization. The granulation of information is attained using the C-Means clustering algorithm. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and the polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by the MSSA and C-Means, whereas the parameter identification is realized via the MSSA and least squares method. The evaluation of the performance of the proposed model was conducted using three representative numerical examples such as gas furnace, NOx emission process data, and Mackey-Glass time series. The proposed model was also compared with the quality of some "conventional" fuzzy models encountered in the literature.

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References

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