Nonlinear System Identification Using Extreme Learning Machine
Ming-Bin Li, Meng Joo Er · 2006
System identification is a very important part in control theory for nonlinear analysis and optimization. In the past years, neural identification of dynamic systems gains great interest because of its powerful mapping capability. In this paper, a learning algorithm for the feedforward neural network named extreme learning machine (ELM) is applied for nonlinear system identification problem. The simulation results show that ELM can achieve very satisfying identification performance and fast learning speed