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Fuzzy Models

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Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering

Part of the book series: Water Science and Technology Library ((WSTL,volume 67))

Abstract

While variables in mathematics usually take numerical values, in fuzzy logic applications, the non-numeric linguistic variables are often used to facilitate the expression of rules and facts. The idea of fuzzy logic is very suitable for engineering application where a precise representation of the real world is sought. In contrast to the statistical-based methods, fuzzy models do not need very strong assumptions and requirements. As far as the engineering application of fuzzy logic is concerned, two approaches are usually followed up: (1) developing fuzzy extensions of the classic methods and models and (2) developing models, which are basically originated by the fuzzy logic. Basic information in fuzzy logic, fuzzy clustering, fuzzy inference systems, and fuzzy regression are the main subjects which are presented in this chapter. Obviously, the related useful MATLAB commands are presented and discussed to support the methods of applied modeling of the presented subjects.

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Authors and Affiliations

  1. College of Agriculture and Natural Resources Irrigation and Reclamation Engineering, University of Tehran, Tehran, Iran

    Shahab Araghinejad

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  1. Shahab Araghinejad
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© 2014 Springer Science+Business Media Dordrecht

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Araghinejad, S. (2014). Fuzzy Models. In: Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering. Water Science and Technology Library, vol 67. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7506-0_7

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