Research on total least squares method based on error entropy criterion

DOI

10.2991/icmeit-17.2017.112How to use a DOI?

Keywords

non-Gaussian; error entropy criterion (EEC); TLS; minimum total error entropy (MTEE).

Abstract

The total least square (TLS) estimation problem of random systems is widely found in many fields of engineering and science, such as signal processing, automatic control, system theory and so on. In the case of linear Gaussian case, a very mature TLS parameter estimation algorithm has been developed. In the non-Gaussian case, the existing research is not much and not deep, the current error entropy criterion (EEC) and the minimum error entropy( MEE) based on EEC method has been paid attention to, the traditional MEE only consider the output data contains the error situation, so it cannot get the optimal solution. In this paper, we consider the inclusion of noise in the input and output data, and deduce the total error entropy criterion (TEEC) and the corresponding TLS method named the minimum total error entropy (MTEE) method The In addition, the derivation method is simulated, and the simulation results show the correctness of the algorithm.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ben Wang
AU  - Xiangyu Kong
AU  - Wei Huang
AU  - Zehao Cao
PY  - 2017/05
DA  - 2017/05
TI  - Research on total least squares method based on error entropy criterion
BT  - Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
PB  - Atlantis Press
SP  - 610
EP  - 617
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-17.2017.112
DO  - 10.2991/icmeit-17.2017.112
ID  - Wang2017/05
ER  -