Modeling and Identification of Complex Systems using Neuro-Fuzzy Techniques.

Authors

M. El-Hosiny, Computers & Systems Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
M. Sherif, Computes & Systems Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
Sabry Saraya, Computers & systems Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.Follow
M. El-Basyouni, Faculty in specific Education, Mansoura University.
Fayez Areed, Computers & Systems Department., Faculty of Engineering., El-Mansoura Universsity., Mansoura., Egypt.

Corresponding Author

Areed, Fayez

Subject Area

Electrical Engineering

Article Type

Original Study

Abstract

In this paper Adaptive-Network-Based Fuzzy Inference System (ANFIS) architecture is presented and has been used as a tool for System Identification. System Identification consists of three related steps: 1:) Structure specification, 2:) Parameter estimation. 3:) Model Validation. These steps are also discussed. We use a new method to determine the structure of the ANFIS model (Fuzzy Curves). Fuzzy Curves Help in determining the number of significant inputs from a number of candidates. We determine the number of membership functions in each input by using the subtractive clustering. Finally this method is tested on two cases.

Recommended Citation

El-Hosiny, M.; Sherif, M.; Saraya, Sabry; El-Basyouni, M.; and Areed, Fayez (2021) "Modeling and Identification of Complex Systems using Neuro-Fuzzy Techniques.," Mansoura Engineering Journal: Vol. 27 : Iss. 1 , Article 6.
Available at: https://doi.org/10.21608/bfemu.2021.142599