Research on total least squares method based on error entropy criterion
Ben Wang, Xiangyu Kong, Wei Huang, Zehao Cao
Available Online May 2017.
- https://doi.org/10.2991/icmeit-17.2017.112How to use a DOI?
- non-Gaussian; error entropy criterion (EEC); TLS; minimum total error entropy (MTEE).
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/icmeit-17.2017.112 ID - Wang2017/05 ER -