Abstract
This contribution aims to enrich the recently introduced kernel-based regularization method for linear system identification. Instead of a single kernel, we use multiple kernels, which can be instances of any existing kernels for the impulse response estimation of linear systems. We also introduce a new class of kernels constructed based on output error (OE) model estimates. In this way, a more flexible and richer representation of the kernel is obtained. Due to this representation the associated hyper-parameter estimation problem has two good features. First, it is a difference of convex functions programming (DCP) problem. While it is still nonconvex, it can be transformed into a sequence of convex optimization problems with majorization minimization (MM) algorithms and a local minima can thus be found iteratively. Second, it leads to sparse hyper-parameters and thus sparse multiple kernels. This feature shows the kernel-based regularization method with multiple kernels has the potential to tackle various problems of finding sparse solutions in linear system identification.
Original language | English |
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Title of host publication | 2012 IEEE 51st Annual Conference on Decision and Control (CDC) |
Publisher | IEEE |
Publication date | 2012 |
Pages | 1500-1505 |
ISBN (Print) | 978-1-4673-2065-8 |
DOIs | |
Publication status | Published - 2012 |
Externally published | Yes |
Event | 51st IEEE Conference on Decision and Control - Maui, HI, United States Duration: 10 Dec 2012 → 13 Dec 2012 Conference number: 51 |
Conference
Conference | 51st IEEE Conference on Decision and Control |
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Number | 51 |
Country/Territory | United States |
City | Maui, HI |
Period | 10/12/2012 → 13/12/2012 |