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Important New Terms and Classifications in Uncertainty and Fuzzy Logic

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Fifty Years of Fuzzy Logic and its Applications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 326))

Abstract

Human cognitive and perception processes have a great tolerance for imprecision or uncertainty. For this reason, the notions of perception and cognition have great importance in solving many decision making problems in engineering, medicine, science, and social science as there are innumerable uncertainties in real-world phenomena. These uncertainties can be broadly classified as either type one uncertainty arising from the random behavior of physical processes or type two uncertainty arising from human perception and cognition processes. Statistical theory can be used to model the former, but lacks the sophistication to process the latter. The theory of fuzzy logic has proven to be very effective in processing type two uncertainty. New computing methods based on fuzzy logic can lead to greater adaptability, tractability, robustness, a lower cost solution, and better rapport with reality in the development of intelligent systems. Fuzzy logic is needed to properly pose and answer queries about quantitatively defining imprecise linguistic terms like middle class, poor, low inflation, medium inflation, and high inflation. Imprecise terms like these in natural languages should be considered to have qualitative definitions, quantitative definitions, crisp quantitative definitions, fuzzy quantitative definitions, type-one fuzzy quantitative definitions, and interval type-two fuzzy quantitative definitions. There can be crisp queries, crisp answers, type-one fuzzy queries, type-one fuzzy answers, interval type-two fuzzy queries, and interval type-two fuzzy answers.

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Author information

Authors and Affiliations

  1. Intelligent Systems Research Laboratory, College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada

    Madan M. Gupta

  2. Maverick Technologies America Inc, Wilmington, DE, USA

    Ashu M. G. Solo

Authors
  1. Madan M. Gupta
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  2. Ashu M. G. Solo
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Correspondence to Madan M. Gupta .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Texas State University, San Marcos, Texas, USA

    Dan E. Tamir

  2. School of Computing and Information Sciences, Florida International University, Miami, Florida, USA

    Naphtali D. Rishe

  3. School of Computing and Information Sciences, The University of South Florida, Tampa, Florida, USA, and Florida International University, Miami, Florida, USA

    Abraham Kandel

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Gupta, M.M., Solo, A.M.G. (2015). Important New Terms and Classifications in Uncertainty and Fuzzy Logic. In: Tamir, D., Rishe, N., Kandel, A. (eds) Fifty Years of Fuzzy Logic and its Applications. Studies in Fuzziness and Soft Computing, vol 326. Springer, Cham. https://doi.org/10.1007/978-3-319-19683-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-19683-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19682-4

  • Online ISBN: 978-3-319-19683-1

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