Title :
Nonlinear Identification Based on Diagonal Recurrent Neural Network and Particle Filter
Author :
Xiaolong, Deng ; Pingfang, Zhou
Author_Institution :
Dept. of Mech. Eng., Jiangsu Coll. of Inf. Technol., Wuxi, China
Abstract :
Diagonal recurrent neural network (DRNN) is widely applied to nonlinear identification. In this paper, the extended Kalman filter and particle filter are firstly combined to train DRNN. Utilizing time windows, a method to evaluate the dynamical performance of DRNN is presented. Network weights of particles are optimized by the resampling algorithm. The high convergent speed and high training precision are obtained by the new algorithm. Simulation results of the nonlinear dynamical identification verify the validity of the new algorithm.
Keywords :
Kalman filters; algorithm theory; nonlinear dynamical systems; particle filtering (numerical methods); recurrent neural nets; diagonal recurrent neural network; extended Kalman filter; high convergent speed; high training precision; new algorithm validity; nonlinear dynamical identification; nonlinear identification based; particle filter; resampling algorithm; Artificial neural networks; Chaos; Computer networks; Delay estimation; Educational institutions; Mechanical engineering; Neurofeedback; Neurons; Particle filters; Recurrent neural networks; diagonal recurrent neural network; nonlinear identification; particle filter; the extended Kalman filter; training algorithm;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.496
