TY - JOUR ID - 7153 TI - Stable Rough Extreme Learning Machines for the Identification of Uncertain Continuous-Time Nonlinear Systems JO - Control and Optimization in Applied Mathematics JA - COAM LA - en SN - 2383-3130 AU - Ahmadi, Ghasem AD - Payame Noor University (PNU), Tehran, Iran Y1 - 2019 PY - 2019 VL - 4 IS - 1 SP - 83 EP - 101 KW - System identification‎ KW - ‎Extreme learning machine‎ KW - ‎Rough-neural network‎ KW - ‎Rough extreme learning machine‎ KW - ‎Lyapunov stability theory DO - 10.30473/coam.2020.51511.1137 N2 - ‎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. UR - https://mathco.journals.pnu.ac.ir/article_7153.html L1 - https://mathco.journals.pnu.ac.ir/article_7153_718a35eb9802054b84ce8e4b9918749d.pdf ER -