By Topic

Apply BPNN with Kalman Filtering to the dynamic system identification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Tung Yung Tsai ; WuFeng Inst. of Technol., Chiayi ; Huang-Wan Chen

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.

Published in:

Machine Learning and Cybernetics, 2008 International Conference on  (Volume:6 )

Date of Conference:

12-15 July 2008