CFP last date
20 May 2024
Reseach Article

A Comparison between Fuzzy Inference Systems for Prediction (with Application to Prices of Fund in Egypt)

by Raafat Fahmy, Hegazy Zaher, Abd Elfattah Kandil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 109 - Number 13
Year of Publication: 2015
Authors: Raafat Fahmy, Hegazy Zaher, Abd Elfattah Kandil
10.5120/19246-0604

Raafat Fahmy, Hegazy Zaher, Abd Elfattah Kandil . A Comparison between Fuzzy Inference Systems for Prediction (with Application to Prices of Fund in Egypt). International Journal of Computer Applications. 109, 13 ( January 2015), 6-11. DOI=10.5120/19246-0604

@article{ 10.5120/19246-0604,
author = { Raafat Fahmy, Hegazy Zaher, Abd Elfattah Kandil },
title = { A Comparison between Fuzzy Inference Systems for Prediction (with Application to Prices of Fund in Egypt) },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 109 },
number = { 13 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume109/number13/19246-0604/ },
doi = { 10.5120/19246-0604 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:44:40.861778+05:30
%A Raafat Fahmy
%A Hegazy Zaher
%A Abd Elfattah Kandil
%T A Comparison between Fuzzy Inference Systems for Prediction (with Application to Prices of Fund in Egypt)
%J International Journal of Computer Applications
%@ 0975-8887
%V 109
%N 13
%P 6-11
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper outlines the basic differences between the Fuzzy logic techniques, including Mamdani , Sugeno fuzzy inference system models and Adaptive Neuro-Fuzzy Inference System (ANFIS) . 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 , Sugeno models and ANFIS are compared with the actual values. Fuzzy inference systems (Mamdani , Sugeno and ANFIS fuzzy models ) can be used to predict the weekly prices of Fund for the Egyptian Market. The application results indicate that (ANFIS) model is better than that of Mamdani and Sugeno . The results of the three fuzzy inference systems (FIS) are compared.

References
  1. Kumar P. & Kumar D. Rainfall-Runoff Modelling of a Watershed. Civil and Environmental Research. 2012; Vol. 2, pp. 35-42.
  2. Bireka L. , Petrovica D. & Boylanb J. A fuzzy Logic Based Approach to Leakage Forecasting in Water Industry. The 31st Annual International Symposium on Forecasting. 2011; pp. 1-12.
  3. Alvisi S. , Mascellani G. , Franchini M. , & B´ardossy A. Water Level Forecasting Through Fuzzy Logic and Artificial Neural Network Approaches. Hydrology and Earth System Sciences. 2006; Vol. 10, Pp. 1–17.
  4. Aqil M. , Kita I. , Yano A. , & Nishiyama S. A Takagi-Sugeno Fuzzy System for the Prediction of River Stage Dynamicsmes. JARQ. 2006; Vol. (4), pp. 369 – 378.
  5. Keskin M. , Taylan E. , & Yilmaz A. Flow Prediction Model with Fuzzy Logic approaches: Dim Stream. International Congress on River Basin Management. 2004; Pp. 439-447.
  6. Mahabir C. , Hicks F. & Robinson F. Forecasting Ice Jam Risk at Fort Mcmurray, AB, using fuzzy logic. International Association of Hydraulic Engineering and Research, New Zealand. 2002; 2nd–6th December, pp. 112-119.
  7. Tektas M. Weather Forecasting Using ANFIS and ARIMA MODELS, A Case Study of Istanbul. Environmental Research, Engineering and Management. 2010; Vol. (51), Pp. 5 – 10.
  8. Kurian C. , George V. , Bhat J. & Aithal R. ANFIS Model for the Time Series Prediction of Interior Daylight Illuminance. AIML Journal. 2006; Vol. 6, pp. 35-40.
  9. Korol T. & Korodi A. An Evaluation of Effectiveness of fuzzy Logic Model in Predicting the Business Bankruptcy. Romanian Journal of Economic Forecasting. 2011; Vol(1), pp. 92–107.
  10. Sajfert Z. , Atanaskovi? P. , Pamu?ar D. & Nikoli? M. Application of Fuzzy Logic into Process of Decision Making Regarding Selection of Managers. African Journal of Business Management. 2012; Vol. 6, pp. 3221-3233.
  11. Chang P. & Wang Y. Fuzzy Delphi and Back-Propagation Model for Sales Forecasting in PCB Industry. Expert Systems with Applications. 2006; Vol. 30, pp. 715–726.
  12. Naieni S. , Makui A. , & Ghousi R. An Approach for Accident Forecasting Using Fuzzy Logic Rules: A Case Mining of Lift Truck Accident Forecasting in One of the Iranian Car Manufacturers. International Journal of Industrial Engineering & Production Research. 2012; Vol. 23, pp. 53-64.
  13. Pasila F. , Ajoy K. & Thiele G. Neuro-Fuzzy Approaches for Forecasting Electrical Load Using Additional Moving Average Window Data Filter on Takagi-Sugeno Type MISO Networks. Journal of Advanced Computational Intelligence and Intelligent Informatics. 2008; Vol. 12. pp. 361-366.
  14. Dankovi´c B. , Jovanovi´c Z. & Anti´c D. Peak Power Optimization Based On Nonlinear Prediction and Fuzzy Logic. FACTA University (NIS) SER. : ELEC. ENERG. 2005; Vol. 18, pp. 431-437.
  15. Ferreira L. , Yanagi-Jr T. , Nããs I. & Lopes M. Development of a Decision Making System Using Fuzzy Logic to Predict Estrus in Dairy Cows. Agricultural Engineering International: the CIGR Ejournal. 2007; Vol. IX. , pp. 1-16.
  16. Zaher H. , Kandil A. & Fahmy R. Comparison of Mamdani and Sugeno Fuzzy Inference Systems for Prediction (With Application to Prices of Fund in Egypt). British Journal of Mathematics & Computer Science. 2014; 4(21): 3014-3022, 2014.
  17. Kaur A. , & Kaur A. Comparison of mamdani-type and sugeno-type fuzzy inference systems for air conditioning system. International Journal of Soft Computing and Engineering (IJSCE). 2012;2:323-325.
  18. Mamdani EH, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies. 1975; 7:1-13.
  19. Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans, on Systems, Man and Cybernetics. 1985; 15:116-132.
  20. Jassbi JJ, Serra PJA, Ribeiro RA, Donati A. A comparison of mamdani and Sugeno inference systems for a space fault detection application. Automation Congress, WAC '06. World. 2006;24-26:1 – 8.
  21. Mendel J. Uncertain rule-based fuzzy inference systems: Introduction and new directions. Prentice-Hall; 2001.
  22. Kisi O. Applicability of Mamdani and Sugeno fuzzy genetic approaches for modeling reference evapotranspiration. Journal of Hydrology. 2013;504:160–170.
  23. Jassbi J, Alavi SH, Serra PJA, Ribeiro RA. Transformation of a Mamdani FIS to First Order Sugeno FIS. Fuzzy Systems Conference, FUZZ-IEEE; 2007.
  24. Alavala C. Fuzzy Logic and Neural Networks Basic Concepts & Application. New Age International (p) Ltd. New Delhi. 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Prices of Fund ANFIS model Fuzzy Inference System (FIS) Fuzzy Logic Mamdani model Sugeno model.