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

El-Hosiny, M.; Sherif, M.; Saraya, Sabry; El-Basyouni, M. R.; Areed, Fayez

doi:10.21608/bfemu.2021.142599

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

Document Type : Research Studies

Authors

¹ Computers & Systems Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.

² Computes & Systems Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.

³ Computers & systems Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.

⁴ Faculty in specific Education, Mansoura University.

⁵ Computers & Systems Department., Faculty of Engineering., El-Mansoura Universsity., Mansoura., Egypt.

10.21608/bfemu.2021.142599

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.

Main Subjects