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International Conference on Adaptive and Intelligent Systems

ICAIS 2011: Adaptive and Intelligent Systems pp 52–63Cite as

An Extended Sliding Mode Learning Algorithm for Type-2 Fuzzy Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6943))

Abstract

Type-2 fuzzy logic systems are an area of growing interest over the last years. The ability to model uncertainties in a better way than type-1 fuzzy logic systems increases their applicability. A new stable on-line learning algorithm for type-2 fuzzy neural networks is proposed in this paper. It can be considered as an extended version of the recently developed on-line learning approaches for type-2 fuzzy neural networks based on the Variable Structure System theory concepts. Simulation results from the identification of a nonlinear system with uncertainties have demonstrated the better performance of the proposed extended algorithm in comparison with the previously reported in the literature sliding mode learning algorithms for both type-1 and type-2 fuzzy neural structures.

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Author information

Authors and Affiliations

  1. Control Systems Department, TU – Sofia campus Plovdiv, 25 Tsanko Dustabanov Str., 4000, Plovdiv, Bulgaria

    Kostadin Shiev, Nikola Shakev, Andon V. Topalov & Sevil Ahmed

  2. Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey

    Okyay Kaynak

Authors
  1. Kostadin Shiev
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  2. Nikola Shakev
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  3. Andon V. Topalov
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  4. Sevil Ahmed
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  5. Okyay Kaynak
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Editor information

Editors and Affiliations

  1. Department of Informatics-Systems, University of Klagenfurt, Universitätsstr. 65-67, 9020, Klagenfurt, Austria

    Abdelhamid Bouchachia

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© 2011 Springer-Verlag Berlin Heidelberg

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Shiev, K., Shakev, N., Topalov, A.V., Ahmed, S., Kaynak, O. (2011). An Extended Sliding Mode Learning Algorithm for Type-2 Fuzzy Neural Networks. In: Bouchachia, A. (eds) Adaptive and Intelligent Systems. ICAIS 2011. Lecture Notes in Computer Science(), vol 6943. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23857-4_9

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  • DOI: https://doi.org/10.1007/978-3-642-23857-4_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23856-7

  • Online ISBN: 978-3-642-23857-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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