title: Adaptive control of nonlinear system using online error minimum neural networks.
abstract: In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly.
authors: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China.
authors: Chao
authors: C
authors: Jia
authors: College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, P.R. China. Electronic address: lixiaoli@hotmail.com.
authors: Xiaoli
authors: X
authors: Li
authors: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China.
authors: Kang
authors: K
authors: Wang
authors: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China.
authors: Dawei
authors: D
authors: Ding
mesh_terms: D000465:Algorithms; D000069550:Machine Learning; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009862:Online Systems
keywords: Adaptive control; ELM; EM-ELM; Neural networks; OS-ELM