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.