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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Bibliographic Details
Published in:Nonlinear dynamics Vol. 104; no. 3; pp. 2515 - 2530
Main Authors: Mohamadipanah, Hossein, Heydari, Mahdi, Chowdhary, Girish
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.05.2021
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-021-06416-0