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Type-2 Fuzzy Logic and the Modelling of Uncertainty in Applications

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Human-Centric Information Processing Through Granular Modelling

Part of the book series: Studies in Computational Intelligence ((SCI,volume 182))

Abstract

Most real world applications contain high levels of uncertainty and imprecision. Sources of the imprecision include sensor noise; variation in actuator performance; linguistic variation between people; temporal modification of expert opinion; and disagreement between experts. Type-2 fuzzy logic is now accepted as a mature technology for coping with this wide variety of sources of uncertainty. This Chapter provides an overview of type-2 fuzzy logic systems providing the reader with an insight into how the various algorithms provide different approaches to modelling uncertainty. We place in context these issues by discussing a number of real world applications that have successfully deployed type-2 fuzzy logic.

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Author information

Authors and Affiliations

  1. De Montfort University, The Gateway, Leicester, LE1 9BH, UK

    Robert John & Simon Coupland

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  1. Robert John
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  2. Simon Coupland
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  1. School of Computer Science, The University of Nottingham, Malaysia Campus, Jalan Broga, 43500, Semenyih Selangor Darul Ehsan, Malaysia

    Andrzej Bargiela

  2. Department of Electrical & Computer Engineering, University of Alberta, AB T6R 2G7, Edmonton, Canada

    Witold Pedrycz

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John, R., Coupland, S. (2009). Type-2 Fuzzy Logic and the Modelling of Uncertainty in Applications. In: Bargiela, A., Pedrycz, W. (eds) Human-Centric Information Processing Through Granular Modelling. Studies in Computational Intelligence, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92916-1_8

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  • DOI: https://doi.org/10.1007/978-3-540-92916-1_8

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  • Online ISBN: 978-3-540-92916-1

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