Comparison of Mamdani and Sugeno Fuzzy Inference Systems for Prediction (With Application to Prices of Fund in Egypt) | Zendy

Hegazy Zaher | Zendy; Abd Kandil | Zendy; Raafat Fahmy | Zendy

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Comparison of Mamdani and Sugeno Fuzzy Inference Systems for Prediction (With Application to Prices of Fund in Egypt)

Author(s) -

Hegazy Zaher,

Abd Kandil,

Raafat Fahmy

Publication year - 2014

Publication title -

british journal of mathematics and computer science

Language(s) - English

Resource type - Journals

ISSN - 2231-0851

DOI - 10.9734/bjmcs/2014/11644

Subject(s) - fuzzy inference system , fuzzy inference , inference , computer science , fuzzy logic , adaptive neuro fuzzy inference system , artificial intelligence , econometrics , fuzzy control system , machine learning , economics

This paper outlines the basic difference between the Mamdani/Sugeno Fuzzy inference systems (FIS) and the actual values. The main motivation behind this research is to assess which approach provides the best performance for predicting prices of Fund. Due to the importance of performance in Economy, the Mamdani and Sugeno models are compared using four types of membership function (MF) generation methods: the Triangular, Trapezoidal, Gaussian and Gbell. Fuzzy inference systems (Mamdani and Sugeno fuzzy mo dels) can be used to predict the weekly prices of Fund for the Egyptian Market. The application results indicate that Sugeno model is better than that of Mamdani. The results of the two fuzzy inference systems (FIS) are compared.

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