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

Abstract

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