NDLI: Filter-based regularisation for impulse response modelling

Content Provider	IET Digital Library
Author	Marconato, Anna Schoukens, Maarten Schoukens, Johan
Abstract	In the last years, the success of kernel-based regularisation techniques in solving impulse response modelling tasks has revived the interest on linear system identification. In this work, an alternative perspective on the same problem is introduced. Instead of relying on a Bayesian framework to include assumptions about the system in the definition of the covariance matrix of the parameters, here the prior knowledge is injected at the cost function level. The key idea is to define the regularisation matrix as a filtering operation on the parameters, which allows for a more intuitive formulation of the problem from an engineering point of view. Moreover, this results in a unified framework to model low-pass, band-pass and high-pass systems, and systems with one or more resonances. The proposed filter-based approach outperforms the existing regularisation method based on the TC and DC kernels, as illustrated by means of Monte Carlo simulations on several linear modelling examples.
Starting Page	194
Ending Page	204
Page Count	11
ISSN	17518644
Volume Number	11
e-ISSN	17518652
Issue Number	Issue 2, Jan (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/11/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2016.0908
Journal	IET Control Theory & Applications
Publisher Date	2016-10-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Band-pass Filter Band-pass System Cost Function Level Covariance Matrices DC Kernels Filter-based Approach Filter-based Regularisation Filtering Operation High Pass Filter High-pass System Identification Impulse Response Modelling Kernel-based Regularisation Technique Linear System Linear System Identification Low-pass Filter Low-pass System Modelling And Identification Monte Carlo Method Monte Carlo Simulation Method Parameter Covariance Matrix Regularisation Matrix Signal Processing Theory Simulation TC Kernels Transient Response
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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