Performance Evaluation of the Maximum Correntropy Criterion in Identification Systems

被引：0
|
作者：
Guimaraes, Joao P. F. [1 ]
Fontes, Aluisio I. R. [1 ]
Rigo, Joilson B. A. [2 ]
Silveira, Luiz F. Q. [2 ]
Martins, Allan M. [2 ]
机构：
[1] Fed Inst Rio Grande do Norte, Comp Engn, Natal, RN, Brazil
[2] Fed Inst Rio Grande do Norte, Elect Engn, Natal, RN, Brazil
来源：
PROCEEDINGS OF THE 2016 IEEE CONFERENCE ON EVOLVING AND ADAPTIVE INTELLIGENT SYSTEMS (EAIS) | 2016年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The System identification explores ways to obtain mathematical models of an unknown system. However, as a result from the intrinsic random nature of system or from the environment noise, it is very hard to find a perfect mathematical representation of a real system. This paper aims to evaluate the Maximum Correntropy Criterion (MCC) performance using the gradient descent and the Fixed-Point. Both methods were compared in different noise scenarios and their behavior with different system models. The importance of the free parameters was also studied on both methods. The results show that the fixed-point has a better performance and are less noise sensitive.
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页码：110 / 113
页数：4
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