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Design of Type-Reduction Strategies for Type-2 Fuzzy Logic Systems using Genetic Algorithms

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Advances in Evolutionary Computing for System Design

Part of the book series: Studies in Computational Intelligence ((SCI,volume 66))

Increasingly, research in the field of fuzzy theory is focusing on fuzzy sets (FSs) whose membership functions are themselves fuzzy. The key concept of such type-2 FSs is the footprint of uncertainty. It provides an extra mathematical dimension that equips type-2 fuzzy logic systems (FLSs) with the potential to outperform conventional (type-1) FLSs. While a type-2 FLS has the capability to model more complex relationships, the output of a type-2 fuzzy inference engine is a type-2 FS that needs to be type-reduced before defuzzification can be performed. Unfortunately, type-reduction is usually achieved using the computationally intensive Karnik-Mendel iterative algorithm. In order for type-2 FLSs to be useful for real-time applications, the computational burden of type-reduction needs to be relieved. This work aims at designing computationally efficient type-reducers using a genetic algorithm (GA). The proposed type-reducer is based on the concept known as equivalent type-1 FSs (ET1FSs), a collection of type-1 FSs that replicates the input-output relationship of a type-2 FLS. By replacing a type-2 FS with a collection of ET1FSs, the type-reduction process then simplifies to deciding which ET1FS to employ in a particular situation. The strategy for selecting the ET1FS is evolved by a GA. Results are presented to demonstrate that the proposed type-reducing algorithm has lower computational cost and may provide better performance than FLSs that employ existing type-reducers.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, National University of Singapore, 4, Engineering Drive 3, 117576, Singapore

    Woei-Wan Tan & Dongrui Wu

Authors
  1. Woei-Wan Tan
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  2. Dongrui Wu
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Editor information

Editors and Affiliations

  1. University of South Australia, Adelaide, Mawson Lakes Campus, 5095, South Australia, Australia

    Lakhmi C. Jain

  2. Computing Laboratory, Wolfson Building Parks Road, OX1 3QD, Oxford, England

    Vasile Palade

  3. National University of Singapore, Block E4-06-08, 4 Engineering Drive 3, 117576, Singapore

    Dipti Srinivasan

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© 2007 Springer-Verlag Berlin Heidelberg

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Tan, WW., Wu, D. (2007). Design of Type-Reduction Strategies for Type-2 Fuzzy Logic Systems using Genetic Algorithms. In: Jain, L.C., Palade, V., Srinivasan, D. (eds) Advances in Evolutionary Computing for System Design. Studies in Computational Intelligence, vol 66. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72377-6_7

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  • DOI: https://doi.org/10.1007/978-3-540-72377-6_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72376-9

  • Online ISBN: 978-3-540-72377-6

  • eBook Packages: EngineeringEngineering (R0)

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