Stability Analysis of Neural Networks-Based System Identification
This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2008-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2008/343940 |
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| _version_ | 1849309264613474304 |
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| author | Talel Korkobi Mohamed Djemel Mohamed Chtourou |
| author_facet | Talel Korkobi Mohamed Djemel Mohamed Chtourou |
| author_sort | Talel Korkobi |
| collection | DOAJ |
| description | This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm. |
| format | Article |
| id | doaj-art-187eb8229a784fa986b5d950f6d47fb1 |
| institution | Kabale University |
| issn | 1687-5591 1687-5605 |
| language | English |
| publishDate | 2008-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-187eb8229a784fa986b5d950f6d47fb12025-08-20T03:54:12ZengWileyModelling and Simulation in Engineering1687-55911687-56052008-01-01200810.1155/2008/343940343940Stability Analysis of Neural Networks-Based System IdentificationTalel Korkobi0Mohamed Djemel1Mohamed Chtourou2Research Unit on Intelligent Control, Design and Optimization of Complex Systems (ICOS), National Engineering School of Sfax (ENIS), University of Sfax, BP W, 3038 Sfax, TunisiaResearch Unit on Intelligent Control, Design and Optimization of Complex Systems (ICOS), National Engineering School of Sfax (ENIS), University of Sfax, BP W, 3038 Sfax, TunisiaResearch Unit on Intelligent Control, Design and Optimization of Complex Systems (ICOS), National Engineering School of Sfax (ENIS), University of Sfax, BP W, 3038 Sfax, TunisiaThis paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.http://dx.doi.org/10.1155/2008/343940 |
| spellingShingle | Talel Korkobi Mohamed Djemel Mohamed Chtourou Stability Analysis of Neural Networks-Based System Identification Modelling and Simulation in Engineering |
| title | Stability Analysis of Neural Networks-Based System Identification |
| title_full | Stability Analysis of Neural Networks-Based System Identification |
| title_fullStr | Stability Analysis of Neural Networks-Based System Identification |
| title_full_unstemmed | Stability Analysis of Neural Networks-Based System Identification |
| title_short | Stability Analysis of Neural Networks-Based System Identification |
| title_sort | stability analysis of neural networks based system identification |
| url | http://dx.doi.org/10.1155/2008/343940 |
| work_keys_str_mv | AT talelkorkobi stabilityanalysisofneuralnetworksbasedsystemidentification AT mohameddjemel stabilityanalysisofneuralnetworksbasedsystemidentification AT mohamedchtourou stabilityanalysisofneuralnetworksbasedsystemidentification |