Deep kernel recursive least-squares algorithm

We present a new kernel-based algorithm for modeling evenly distributed multidimensional datasets that does not rely on input space sparsification. The presented method reorganizes the typical single-layer kernel-based model into a deep hierarchical structure, such that the weights of a kernel model...

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Vydané v:Nonlinear dynamics Ročník 104; číslo 3; s. 2515 - 2530
Hlavní autori: Mohamadipanah, Hossein, Heydari, Mahdi, Chowdhary, Girish
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.05.2021
Springer Nature B.V
Predmet:
Algorithms
Automotive Engineering
Classical Mechanics
Control
Dynamical Systems
Engineering
Kernels
Mechanical Engineering
Model accuracy
Original Paper
Structural hierarchy
Vibration
Deep hierarchical structure
Kernel recursive least square
Multidimensional dataset
ISSN:0924-090X, 1573-269X
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Shrnutí:We present a new kernel-based algorithm for modeling evenly distributed multidimensional datasets that does not rely on input space sparsification. The presented method reorganizes the typical single-layer kernel-based model into a deep hierarchical structure, such that the weights of a kernel model over each dimension are modeled over its adjacent dimension. We show that modeling weights in the suggested structure leads to significant computational speedup and improved modeling accuracy.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-021-06416-0