@inproceedings{22af446f08e14f7b87cce57aecce74f2,
title = "An adaptive system identification algorithm with a general performance index based on entropy optimization",
abstract = "This paper presents an entropy minimization algorithm for nonlinear system identification based on the information theory. The Parzen windowing estimator is used to approximate the entropy when the probability density functions of the variances can not be known as a priori or the variances are not realistically expressed with the traditional probability density functions. A general performance index based on the information entropy is discussed in this paper. Minimizing the performance index adopted can make the desired output of the adaptive system being tracked directly by the output of the neural network identifier. Furthermore, this performance index can be easily extended when treating other control problems. The performance of the entropy optimal algorithm is shown by several simulations with backpropagation neural networks.",
keywords = "Backpropagation, Entropy optimization, Identification, Neural networks, Parzen windowing",
author = "Liu Yan and Ren Xuemei and Wang Zibin and Na Jing",
year = "2007",
doi = "10.1109/CHICC.2006.4347327",
language = "English",
isbn = "7900719229",
series = "Proceedings of the 26th Chinese Control Conference, CCC 2007",
pages = "270--274",
booktitle = "Proceedings of the 26th Chinese Control Conference, CCC 2007",
note = "26th Chinese Control Conference, CCC 2007 ; Conference date: 26-07-2007 Through 31-07-2007",
}