Hegazy Zaher

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

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.

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

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.

Estimation of Pareto Parameters Using a Fuzzy Least-Squares Method and Other Known Techniques with a Comparison

The purpose of this paper is to obtain the fuzzy least-squar es estimator for the two-parameter Pareto distribution and to compare the fuzzy estimator with different types of estimators. The trimmed linear moments (TL-moments), linear moments (L-moments) and linear quantile moments (LQ-moments) formulas will be obtained for the two-parameter Pareto distribution and the TL-moments estimator, L-moments estimator and LQ-moments estimator will be derived for the Pareto distribution. Numerical comparisons between the proposed method and the existing methods are implemented. According to these comparisons, it is suggested that the proposed fuzzy least-squares estimator is preferable all times.

A Global Indicator for Measuring the Efficiency of Machine Learning Classifier Based on Multi-Criteria Approach

DOI: 10.9734/BJMCS/2015/16358 Editor(s): (1) Anonymous. (2) Tian-Xiao He, Department of Mathematics and Computer Science, Illinois Wesleyan University, USA. Reviewers: (1) Hiram Ponce, Faculty Engineering, Universidad Panamericana, Mexico. (2) Anonymous, India. (3) Scheila de Avila e Silva, Universidade de Caxias do Sul, Brazil. (4) Li, Xing, Department of Health Sciences Research, Mayo Clinic College, USA. Complete Peer review History: http://www.sciencedomain.org/review-history.php?iid=1034&id=6&aid=8841