Targeted on-line modeling for an extended Kalman filter using artificial neural networks

被引：0
|
作者：
Stubberud, SC [1 ]
Owen, MW [1 ]
机构：
[1] Orincon Corp, San Diego, CA USA
来源：
IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE | 1998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：
In this paper, the authors compare implementation techniques of an extended Kalman filter that is augmented by an artificial neural network that trains on-line. The purpose of the neural network is to model mismodeled dynamics of the system that are used in the process of the extended Kalman filter. The authors compare using a neural network that augments the entire model to a neural network that targets the dynamics of specific system states. The idea is to show that targeting specific states will reduce computations while maintaining a high degree of effectiveness.
引用
收藏
页码：1019 / 1023
页数：5
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