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Fuzzy Systems

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Encyclopedia of Machine Learning and Data Mining
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A fuzzy system is a computing framework based on the concepts of the theory of fuzzy sets, fuzzy rules, and fuzzy inference. It is structured in four main components: a knowledge base, a fuzzification interface, an inference engine, and a defuzzification interface. The knowledge base consists of a rule base defined in terms of fuzzy rules, and a database that contains the definitions of the linguistic terms for each input and output linguistic variable. The fuzzification interface transforms the (crisp) input values into fuzzy values, by computing their membership to all linguistic terms defined in the corresponding input domain. The inference engine performs the fuzzy inference process, by computing the activation degree and the output of each rule. The defuzzification interface computes the (crisp) output values by combining the output of the rules and performing a specific transformation.

Fuzzy systems can be classified in different categories. The most widely used are the Mamdani...

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Recommended Reading

  • Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 7(1):1–13

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  • Sugeno M (1985) Industrial applications of fuzzy control. Elsevier Science Publishers, New York

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(2017). Fuzzy Systems. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_322

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