DocumentCode
1647040
Title
A Dynamic Parzen Window Approach Based on Error-entropy Minimization Algorithm for Supervised Training of Nonlinear Adaptive System
Author
Zibin, Wang ; Xuemei Ren ; Yan, Xuemei Liu
Author_Institution
Beijing Inst. of Technol., Beijing
fYear
2007
Firstpage
222
Lastpage
226
Abstract
This paper presents a dynamic Parzen window estimator in the MEE approach for supervised training of nonlinear adaptive system. By adjusting the Parzen window width dynamically so that the overall information force (OIF) among error-samples of each step is as large as possible, the training speed is accelerated and the error is reduced. The simulation result has proved the effectiveness and robustness of this algorithm.
Keywords
adaptive systems; entropy; learning (artificial intelligence); minimisation; nonlinear dynamical systems; dynamic Parzen window estimator; error-entropy minimization algorithm; nonlinear adaptive system; overall information force; Adaptive systems; Control systems; Data mining; Entropy; Error correction; Kernel; Mean square error methods; Minimization methods; Nonlinear dynamical systems; Probability density function; Dynamic Parzen window approach; Error-entropy minimization (MEE); Information Theoretic Learning (ITL);
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference, 2007. CCC 2007. Chinese
Conference_Location
Hunan
Print_ISBN
978-7-81124-055-9
Electronic_ISBN
978-7-900719-22-5
Type
conf
DOI
10.1109/CHICC.2006.4347162
Filename
4347162
Link To Document