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Design of T–S Fuzzy Classifier via Linear Matrix Inequality Approach

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Fuzzy Systems and Knowledge Discovery (FSKD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3613))

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Abstract

A linear matrix inequality approach to designing accurate classifier with a compact T–S(Takagi–Sugeno) fuzzy-rule is proposed, in which all the elements of the T–S fuzzy classifier design problem have been moved in parameters of a LMI optimization problem. Two-step procedure is used to effectively design the T–S fuzzy classifier with many tuning parameters: antecedent part and consequent part design. Then two LMI optimization problems are formulated in both parts and solved efficiently by using interior-point method. Iris data is used to evaluate the performance of the proposed approach. From the simulation results, the proposed approach showed superior performance over other approaches.

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Yonsei University, Seodaemun-gu, Seoul, 120-749, Korea

    Moon Hwan Kim, Jin Bae Park & Ho Jae Lee

  2. School of Electronic and Information Engineering, Kunsan National University, Kunsan, Chonbuk, 573-701, Korea

    Young Hoon Joo

Authors
  1. Moon Hwan Kim
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  2. Jin Bae Park
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  3. Young Hoon Joo
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  4. Ho Jae Lee
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

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© 2005 Springer-Verlag Berlin Heidelberg

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Kim, M.H., Park, J.B., Joo, Y.H., Lee, H.J. (2005). Design of T–S Fuzzy Classifier via Linear Matrix Inequality Approach. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_53

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  • DOI: https://doi.org/10.1007/11539506_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28312-6

  • Online ISBN: 978-3-540-31830-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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