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Extreme Learning Machine (ELM) is an emerging learning paradigm for nonlinearregression problems and has shown its effectiveness in the machine learningcommunity. An important feature of ELM is that the learning speed is extremelyfast thanks to its random projection preprocessing step. This feature is takenadvantage of in designing an online parameter estimation algorithm fornonlinear dynamic systems in this paper. The ELM type random projection and anonlinear transformation in the hidden layer and a linear output layer isconsidered as a generalized model structure for a given nonlinear system and aparameter update law is constructed based on Lyapunov principles. Simulationresults on a DC motor and Lorentz oscillator show that the proposed algorithmis stable and has improved performance over the online-learning ELM algorithm.

Lyapunov Method Based Online Identification of Nonlinear Systems Using Extreme Learning Machines