Stable Rough Extreme Learning Machines for the Identification of Uncertain Continuous-Time Nonlinear Systems

Rough extreme learning machines (RELMs) are rough-neural networks with one hidden layer where the parameters between the inputs and hidden neurons are arbitrarily chosen and never updated. In this paper, we propose RELMs with a stable online learning algorithm for the identification of continuous-time nonlinear systems in the presence of noises and uncertainties, and we prove the global asymptotically convergence of the proposed learning algorithm using the Lyapunov stability theory. Then, we use the proposed methodology to identify the chaotic systems of Duffing’s oscillator and Lorentz system. Simulation results show the efficiency of the proposed model.