Skip to content
Research Article Open access CC BY 3.0

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

Hegazy Zaher, Abd Elfattah Kandil, Raafat Fahmy

Journal of Advances in Mathematics and Computer Science · pp. 3014–3022 · Published 16 Aug 2014

10.9734/BJMCS/2014/11644

Abstract

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 models) 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.

Fuzzy Inference System (FIS) fuzzy logic Mamdani FIS Sugeno FIS prices of fund.

Cited by 7

Analysis and Comparison of Different Fuzzy Inference Systems Used in Decision Making for Secondary Users in Cognitive Radio Network

Shrivishal Tripathi, Ashish Upadhyay, Shashank Kotyan · Wireless Personal Communications · 2018

An image encryption method based on chaotic system exploiting fuzzy system and arithmetic coding

Seyedeh Razieh Mahmoudinejad Dezfuli, Mohammad Kheyrandish · Multimedia Tools and Applications · 2022

Fuzzy Logic-Based DSE Engine: Reconfiguration for Optimization of Multicore Architectures

Iqra Farhat, Muhammad Yasir Qadri, Nadia N. Qadri · Journal of Circuits, Systems and Computers · 2016

Optimal fuzzy‐based power management for real time application in a hybrid generation system

Mehdi Rouholamini, Mohsen Mohammadian, Caisheng Wang · IET Renewable Power Generation · 2017

Discrete quality factors aware channel scheduling in Cognitive Radio Ad-hoc Networks

Ramesh Dasari, N. Venkatram · Journal of Ambient Intelligence and Humanized Computing · 2021

Article metrics

Real usage data collected on this platform.

5

Page views

0

PDF downloads

0

Outbound clicks

7

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.