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Learning Rule for TSK Fuzzy Logic Systems Using Interval Type-2 Fuzzy Subtractive Clustering

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Simulated Evolution and Learning (SEAL 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7673))

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Abstract

The paper deals with an approach to model TSK fuzzy logic systems (FLS), especially interval type-2 TSK FLS, using interval type-2 fuzzy subtractive clustering (IT2-SC). The IT2-SC algorithm is combined with least square estimation (LSE) algorithms to pre-identify a type-1 FLS form from input/output data. Then, an interval type-2 TSK FLS can be obtained by considering the membership functions of its existed type-1 counterpart as primary membership functions and assigning uncertainty to cluster centroids, standard deviation of Gaussian membership functions and consequence parameters. Results is shown in comparison with the approach based on type-1 subtractive clustering algorithm.

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Author information

Authors and Affiliations

  1. Department of Information Systems, Faculty of Information Technology, Le Quy Don Technical University, No 100, Hoang Quoc Viet, Hanoi, Vietnam

    Binh Huy Pham, Hai Trung Ha & Long Thanh Ngo

Authors
  1. Binh Huy Pham
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  2. Hai Trung Ha
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  3. Long Thanh Ngo
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Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Le Quy Don Technical University, 100 Hoang Quoc Viet Street, Cau Giay District,, Hanoi, Vietnam

    Lam Thu Bui

  2. School of Computer Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798,, Singapore, Singapore

    Yew Soon Ong

  3. Faculty of Information Technology,, Hanoi University, km9 Nguyen Trai Road, Hanoi, Vietnam

    Nguyen Xuan Hoai

  4. Graduate School of Engineering, Department of Computer Science, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  5. School of Electrical and Electronic Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

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© 2012 Springer-Verlag Berlin Heidelberg

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Pham, B.H., Ha, H.T., Ngo, L.T. (2012). Learning Rule for TSK Fuzzy Logic Systems Using Interval Type-2 Fuzzy Subtractive Clustering. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_43

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  • DOI: https://doi.org/10.1007/978-3-642-34859-4_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34858-7

  • Online ISBN: 978-3-642-34859-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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