Modeling and Identification of Complex Systems using Neuro-Fuzzy Techniques.
El-Hosiny, M., Sherif, M., Saraya, S., El-Basyouni, M., Areed, F. (2021). Modeling and Identification of Complex Systems using Neuro-Fuzzy Techniques.. EKB Journal Management System, 27(1), 45-54. doi: 10.21608/bfemu.2021.142599
M. El-Hosiny; M. Sherif; Sabry Saraya; M. R. El-Basyouni; Fayez Areed. "Modeling and Identification of Complex Systems using Neuro-Fuzzy Techniques.". EKB Journal Management System, 27, 1, 2021, 45-54. doi: 10.21608/bfemu.2021.142599
El-Hosiny, M., Sherif, M., Saraya, S., El-Basyouni, M., Areed, F. (2021). 'Modeling and Identification of Complex Systems using Neuro-Fuzzy Techniques.', EKB Journal Management System, 27(1), pp. 45-54. doi: 10.21608/bfemu.2021.142599
El-Hosiny, M., Sherif, M., Saraya, S., El-Basyouni, M., Areed, F. Modeling and Identification of Complex Systems using Neuro-Fuzzy Techniques.. EKB Journal Management System, 2021; 27(1): 45-54. doi: 10.21608/bfemu.2021.142599

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

MEJ- Mansoura Engineering Journal
Article 6, Volume 27, Issue 1, March 2002, Page 45-54  XML PDF (525.95 K)
Document Type: Research Studies
DOI: 10.21608/bfemu.2021.142599
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Authors
M. El-Hosiny* 1; M. Sherif* 2; Sabry Saraya email 3; M. R. El-Basyouni4; Fayez Areed* 5
1Computers & Systems Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
2Computes & Systems Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
3Computers & systems Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
4Faculty in specific Education, Mansoura University.
5Computers & Systems Department., Faculty of Engineering., El-Mansoura Universsity., Mansoura., Egypt.
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. 
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