A novel extended kernel recursive least squares algorithm

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,...

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Vydáno v:Neural networks Ročník 32; s. 349 - 357
Hlavní autoři: Zhu, Pingping, Chen, Badong, Príncipe, José C.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Ltd 01.08.2012
Témata:
Algorithms
Computer Simulation
Extended kernel RLS algorithm
Extended-RLS algorithms
Kalman filter
Kalman filters
Kernels
Least squares method
Least-Squares Analysis
Linear Models
Mathematical models
Motor Vehicles
Neural Networks (Computer)
Nonlinear Dynamics
Normal Distribution
Recursive
RLS algorithm
State estimation
Support Vector Machine
Tracking
Tracking performance
Extended-RLS algorithms
RLS algorithm
Tracking performance
Extended kernel RLS algorithm
Kalman filter
ISSN:0893-6080, 1879-2782, 1879-2782
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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2011.12.006