Title :
Modeling method for nonlinear stochastic dynamic system based on neural network and extended Kalman filter
Author :
Qin, Zu Xu ; Zhang, Hong Yue
Author_Institution :
Dept. of Autom. Control, Beijing Univ. of Aeronaut. & Astronaut., China
Abstract :
This paper presents a modelling method for nonlinear stochastic dynamic system (NSDS) modelling based on a neural network and an extended Kalman filter (EKP). Using this method, the data contaminated by noise can be filtered by the EKP. A dynamic neural network (DNN) which is a good approximation to the deterministic part of the NSDS can be obtained. Meanwhile the DNN can be used as a state estimator for the NSDS
Keywords :
Kalman filters; modelling; neural nets; nonlinear dynamical systems; state estimation; stochastic systems; deterministic system; dynamic neural network; extended Kalman filter; modelling method; nonlinear stochastic dynamic system; state estimator; Backpropagation; Equations; Kalman filters; Neural networks; Neurons; Nonlinear dynamical systems; Pollution measurement; State estimation; Stochastic resonance; Stochastic systems;
Conference_Titel :
Industrial Technology, 1994., Proceedings of the IEEE International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
0-7803-1978-8
DOI :
10.1109/ICIT.1994.467035
