DocumentCode
2801084
Title
Adaptive system training based on minimum error entropy
Author
Yan, Wang ; Weiguang, Guo ; Hanwei, Guo
Author_Institution
Coll. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., Changsha, China
Volume
2
fYear
2003
fDate
8-13 Oct. 2003
Firstpage
1245
Abstract
Supervised adaptive system learning based on minimum error entropy method is studied in this article. To measure the information contained in error samples, Renyi´s entropy is estimated with Parzen windowing. While MEE suffers from the high computational burden, so a segmentation method is brought forward to release it. MLP training base on MEE is derived, and MEE training for signal prediction is compared with MSE method. Simulation results verify the effectiveness of MEE method.
Keywords
adaptive systems; learning (artificial intelligence); learning systems; minimum entropy methods; multilayer perceptrons; MLP training; MSE method; Parzen windowing; Renyi entropy; adaptive systems; learning systems; minimum error entropy methods; signal prediction; supervised learning; Abstracts; Adaptive systems; Computational modeling; Computer errors; Computer networks; Educational institutions; Entropy; Kernel; Neural networks; Parametric statistics;
fLanguage
English
Publisher
ieee
Conference_Titel
Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003 IEEE International Conference on
Print_ISBN
0-7803-7925-X
Type
conf
DOI
10.1109/RISSP.2003.1285770
Filename
1285770
Link To Document