Skip to main content

A Kernel Design Approach to Improve Kernel Subspace Identification

Pilario, Karl Ezra S., Cao, Yi, Shafiee, Mahmood (2021) A Kernel Design Approach to Improve Kernel Subspace Identification. IEEE Transactions on Industrial Electronics, 68 (7). pp. 6171-6180. ISSN 0278-0046. (doi:10.1109/TIE.2020.2996142) (KAR id:87317)

PDF Author's Accepted Manuscript
Language: English
Download (5MB) Preview
[thumbnail of IEEE Electronics.pdf]
Preview
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL
http://dx.doi.org/10.1109/TIE.2020.2996142

Abstract

Subspace identification methods, such as canonical variate analysis (CVA), are noniterative tools suitable for the state-space modeling of multi-input, multi-output processes, e.g., industrial processes, using input–output data. To learn nonlinear system behavior, kernel subspace techniques are commonly used. However, the issue of kernel design must be given more attention because the type of kernel can influence the kind of nonlinearities that the model can capture. In this article, a new kernel design is proposed for CVA-based identification, which is a mixture of a global and local kernel to enhance generalization ability and includes a mechanism to vary the influence of each process variable into the model response. During validation, model hyper-parameters were tuned using random search. The overall method is called feature-relevant mixed kernel CVA (FR-MKCVA). Using an evaporator case study, the trained FR-MKCVA models show a better fit to observed data than those of single-kernel CVA, linear CVA, and neural net models under both interpolation and extrapolation scenarios. This work provides a basis for future exploration of deep and diverse kernel designs for system identification.

Item Type: Article
DOI/Identification number: 10.1109/TIE.2020.2996142
Uncontrolled keywords: Kernel canonical variate analysis, kernel principal components analysis, Newell–Lee evaporator, random search, system identification
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Mahmood Shafiee
Date Deposited: 25 Mar 2021 09:09 UTC
Last Modified: 26 Mar 2021 10:39 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/87317 (The current URI for this page, for reference purposes)
Pilario, Karl Ezra S.: https://orcid.org/0000-0001-5448-0909
Cao, Yi: https://orcid.org/0000-0003-2360-1485
Shafiee, Mahmood: https://orcid.org/0000-0002-6122-5719
  • Depositors only (login required):

Downloads

Downloads per month over past year