Identification of nonlinear dynamic system using machine learning techniques

Abstract

Identification of nonlinear systems finds extensive applications in control design and stability analysis. To identify complex nonlinear systems, the neural network has drawn the attention of many researchers due to its broad application area. In this paper, an improved identification method based on robust regularised exponentially extended random vector functional link network (RERVFLN) has been proposed for nonlinear system identification. The input is extended using trigonometric expansion which increases the accuracy of the algorithm. To verify the accuracy of the proposed model, some benchmark Monte Carlo simulations are carried out through simulation study and the obtained results are compared with some established techniques such as original RVFLN, ELM, and LMS. Prediction accuracy of the proposed method RERVFLN is higher than the normal RVFLN for different nonlinear systems which is clear from the performance evaluation section.