Recurrent online kernel recursive least square algorithm for nonlinear modeling
In this paper, we proposed a recurrent kernel recursive least square (RLS) algorithm for online learning. In classical kernel methods, the kernel function number grows as the number of training sample increases, which makes the computational cost of the algorithm very high and only applicable for of...
Main Authors: | Fan, Haijin, Song, Qing, Xu, Zhao |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Conference Paper |
Language: | English |
Published: |
2013
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/101013 http://hdl.handle.net/10220/16315 |
Similar Items
-
Online prediction of time series data with recurrent kernels
by: Xu, Zhao, et al.
Published: (2013) -
An information theoretic kernel algorithm for robust online learning
by: Fan, Haijin, et al.
Published: (2013) -
Investigation of least mean square adaptive algorithm to mitigate the non-linearity in power amplifier
by: Dorle Nikhil
Published: (2018) -
Orthogonal least squares based complex-valued functional link network
by: Amin, Md. Faijul, et al.
Published: (2013) -
Localized, adaptive recursive partial least squares regression for dynamic system modeling
by: Brown, Steven D., et al.
Published: (2013)