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Methodological Identification of Information Granules-Based Fuzzy Systems by Means of Genetic Optimization

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Rough Sets and Current Trends in Computing (RSCTC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4259))

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Abstract

In this study, we introduce an information granules-based fuzzy systems and a methodological identification by means of genetic optimization to carry out the model identification of complex and nonlinear systems. Information granulation realized with Hard C-Means clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions in the premise part and the initial values of polynomial functions in the consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method. The design methodology emerges as a hybrid structural optimization and parametric optimization. Especially, genetic algorithms (GAs) and HCM clustering are used to generate the structurally as well as parametrically optimized fuzzy model. To identify the structure and parameters of fuzzy model we exploit the methodologies of a respective and consecutive identification by means of genetic algorithms. The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, The University of Suwon, San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, South Korea

    Sung-Kwun Oh & Keon-Jun Park

  2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G6, Canada

    Witold Pedrycz

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Witold Pedrycz

Authors
  1. Sung-Kwun Oh
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  2. Keon-Jun Park
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  3. Witold Pedrycz
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Editor information

Editors and Affiliations

  1. Faculty of Economics, University of Catania, Corso Italia, 55, 95129, Catania, Italy

    Salvatore Greco

  2. Graduate School of Engineering, Department of Electrical Engineering and Computer Sciences, University of Hyogo, 2167 Shosha, 671-2280,, Himeji, Hyogo, Japan

    Yutaka Hata

  3. Department of Medical Informatics, Faculty of Medicine, Shimane University, 89-1 Enya-cho, Izumo, 693-8501, Shimane, Japan

    Shoji Hirano

  4. Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, 1-3, Machikaneyama, Toyonaka, 560-8531, Osaka, Japan

    Masahiro Inuiguchi

  5. Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 305-8573, Ibaraki, Japan

    Sadaaki Miyamoto

  6. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Hung Son Nguyen

  7. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

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© 2006 Springer-Verlag Berlin Heidelberg

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Oh, SK., Park, KJ., Pedrycz, W. (2006). Methodological Identification of Information Granules-Based Fuzzy Systems by Means of Genetic Optimization. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_49

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  • DOI: https://doi.org/10.1007/11908029_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47693-1

  • Online ISBN: 978-3-540-49842-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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