TY - GEN
T1 - A dynamic parzen window approach based on error-entropy minimization algorithm for supervised training of nonlinear adaptive system
AU - Wang, Zibin
AU - Ren, Xuemei
AU - Liu, Yan
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Dynamic parzen window approach
KW - Error-entropy minimization (MEE)
KW - Information Theoretic Learning (ITL)
UR - https://www.scopus.com/pages/publications/37749046264
U2 - 10.1109/CHICC.2006.4347162
DO - 10.1109/CHICC.2006.4347162
M3 - Conference contribution
AN - SCOPUS:37749046264
SN - 7900719229
SN - 9787900719225
T3 - Proceedings of the 26th Chinese Control Conference, CCC 2007
SP - 222
EP - 226
BT - Proceedings of the 26th Chinese Control Conference, CCC 2007
T2 - 26th Chinese Control Conference, CCC 2007
Y2 - 26 July 2007 through 31 July 2007
ER -