Title :
Research on nonlinear system identification based on input linearization dynamic recurrent neural network
Author :
Du, Yun ; Sun, Hui-qin ; Meng, Fan-hua ; Zhang, Su-Ying ; Tian, Qiang
Author_Institution :
Hebei Univ. of Sci. & Technol., Shijiazhuang, China
Abstract :
In this paper, it studies the problems of the on-line identification on the nonlinear and time-lag SISO dynamic system. It puts forward the recurrent structure to linearize the input neurons of the neural network which can describe the feasibility of the algorithm, so the neural network has the dynamic on-line identification capability. Simulation results show that the input linearization dynamic recurrent network has a strong self-adaptability and robustness. It gives a new method for SISO nonlinear dynamic system identification.
Keywords :
MIMO systems; identification; linear systems; nonlinear systems; recurrent neural nets; SISO nonlinear dynamic system identification; input linearization dynamic recurrent neural network; nonlinear system identification; online identification; time-lag SISO dynamic system; Artificial neural networks; Equations; Mathematical model; Neurons; Nonlinear dynamical systems; Training; Dynamic recurrent; Input linearization; Neural network; On-line identification;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5580651