Authors:
Talel Korkobi
1
;
Mohamed Djemel
1
and
Mohamed Chtourou
2
Affiliations:
1
Institute of Problem Solving, XYZ University, Intelligent Control, design & Optimization of complex Systems, National Engineering School of Sfax, Tunisia
;
2
Intelligent Control, design & Optimization of complex Systems, National Engineering School of Sfax, Tunisia
Keyword(s):
Stability, neural networks, identification, backpropagation algorithm, constrained learning rate, Lyapunov approach.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Neural Networks Based Control Systems
Abstract:
This paper presents a stable neural sytem identification for nonlinear systems. An input output discrete time representation is considered. No a priori knowledge about the nonlinearities of the system is assumed. 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 phenomenon during the learning process is avoided. A Lyapunov analysis is made in order to extract the new updating formulation which contain a set of inequality constraints. In the constrained learning rate algorithm, the learning rate is updated at each iteration by an equation derived using the stability conditions. As a case study, identification of two discrete time systems is considered to demonstrate the effectiveness of the proposed algorithm. Simulation results concerning the considered systems are presented.