Stability Analysis of Neural Networks-Based System Identification. (11th January 2009)

• Record Type: Journal Article
• Authors: Korkobi, Talel; Djemel, Mohamed; Chtourou, Mohamed
• Other Names: Musilek Petr Academic Editor.
• Is Part Of: Modelling and simulation in engineering. Volume 2008(2008)
• Publication Date: 2009-01-11
• Subjects: Engineering -- Simulation methods -- Periodicals; Engineering -- Mathematical models -- Periodicals; 620.004
• DOI: 10.1155/2008/343940
• Languages: English
• ISSNs: 1687-5591

Abstract

Abstract : 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.