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

by Raafat Fahmy, Hegazy Zaher, Abd Elfattah Kandil
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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.

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