A novel extended kernel recursive least squares algorithm

被引：22

作者：

Zhu, Pingping ^{[1
]}

Chen, Badong ^{[1
]}

Principe, Jose C. ^{[1
]}

机构：

[1] Univ Florida, Dept Elect & Comp Engn, Computat NeuroEngn Lab CNEL, Gainesville, FL 32611 USA

来源：

NEURAL NETWORKS | 2012年 / 32卷

基金：

美国国家科学基金会;

关键词：

Kalman filter; RLS algorithm; Extended-RLS algorithms; Extended kernel RLS algorithm; Tracking performance; SYSTEMS;

D O I：

10.1016/j.neunet.2011.12.006

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, a novel extended kernel recursive least squares algorithm is proposed combining the kernel recursive least squares algorithm and the Kalman filter or its extensions to estimate or predict signals. Unlike the extended kernel recursive least squares (Ex-KRLS) algorithm proposed by Liu, the state model of our algorithm is still constructed in the original state space and the hidden state is estimated using the Kalman filter. The measurement model used in hidden state estimation is learned by the kernel recursive least squares algorithm (KRLS) in reproducing kernel Hilbert space (RKHS). The novel algorithm has more flexible state and noise models. We apply this algorithm to vehicle tracking and the nonlinear Rayleigh fading channel tracking, and compare the tracking performances with other existing algorithms. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：349 / 357

页数：9

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