APPLY BPNN WITH KALMAN FILTERING TO THE DYNAMIC SYSTEM IDENTIFICATION
System identification is an important area in control system. In this paper we discuss some of the reasons caused the slow convergence for BPNN and the effect of the number of neurons in the hidden layer when apply BPNN with Kalman Filtering to dynamic system identification. BPNN is base on LMS, and uses steepest descent method to find the optimum weighting connects to the adjacent layer. It always consumes much of time while training, and not easy to get a global optimal value while applying to on-line training. Kalman Filtering is a better linear and discrete method for parameters estimation. By this way to solve a problem, it can involve the initial conditions, and also can apply to stationary and non-stationary system. So, applying BPNN and Kalman Filtering together to the dynamic system identification, it will get a satisfactory result both on convergent efficiency and stable.
BPNN KalmanFiltering System identification
TUNG YUNG TSAI HUANG-WAN CHEN
WuFeng Institute of Technology, Chia-yi, Taiwan
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
3189-3193
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）