.png){kind=icon}
Return
Multikernel Least Mean Square Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2013.2272594.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The multikernel least-mean-square algorithm is introduced for adaptive estimation of vector-valued nonlinear and nonstationary signals. This is achieved by mapping the multivariate input data to a Hilbert space of time-varying vector-valued functions, whose inner products (kernels) are combined in an online fashion. The proposed algorithm is equipped with novel adaptive sparsification criteria ensuring a finite dictionary, and is computationally efficient and suitable for nonstationary environments. We also show the ability of the proposed vector-valued reproducing kernel Hilbert space to serve as a feature space for the class of multikernel least-squares algorithms. The benefits of adaptive multikernel (MK) estimation algorithms are illuminated in the nonlinear multivariate adaptive prediction setting. Simulations on nonlinear inertial body sensor signals and nonstationary real-world wind signals of low, medium, and high dynamic regimes support the approach.
Keywords:
Adaptive sparsification
kernel methods
least mean square (LMS)
multiple kernels
vector RKHS
wind prediction
Journal
IF:
8.9
Papers:
7.6K
Citations:
7.2W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSave
View more