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
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;
Conference_Titel :
Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003 IEEE International Conference on
Print_ISBN :
0-7803-7925-X
DOI :
10.1109/RISSP.2003.1285770
