System Identification Using Intelligent Algorithms | ||||
| International Conference on Aerospace Sciences and Aviation Technology | ||||
| Article 73, Volume 13, AEROSPACE SCIENCES & AVIATION TECHNOLOGY, ASAT- 13, May 26 – 28, 2009, May 2009, Page 1-13 PDF (205.86 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/asat.2009.23751 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Zaki B. Nossair; A. A. Madkour; M. A. Awadalla; M. M. Abdulhady | ||||
| Department of Computer Science, University of Helwan, Helwan, Egypt, Faculty of Engineering, Helwan University. | ||||
| Abstract | ||||
| This research presents an investigation into the development of system identification using intelligent algorithms. A simulation platform of a flexible beam vibration using finite difference (FD) method is used to demonstrate the capabilities of the identification algorithms. A number of approaches and algorithms for system identifications are explored and evaluated. These identification approaches using (a) traditional Recursive Least Square (RLS) filter, (b) Genetic Algorithms (GAs) (c) Adaptive Neuro_Fuzzy Inference System (ANFIS) model (d) General Regression Neural Network (GRNN) and (e)Bees Algorithm (BA). The above algorithms are used to estimate a linear discrete second order model for the flexible beam vibration. The model is implemented, tested and validated to evaluate and demonstrate the merits of the algorithms for system identification. Finally, a comparative performance of error convergence of the algorithms is presented and discussed. | ||||
| Keywords | ||||
| system identification; adaptive control; intelligent identification; recursive least squares algorithm; Genetic Algorithm; ANFIS; Bees Algorithm | ||||
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