A shift in paradigm for system identification

ABSTRACT

System identification is a mature research area with well established paradigms, mostly based on classical statistical methods. Recently, there has been considerable interest in so called kernel-based regularisation methods applied to system identification problem. The recent literature on this is extensive and at times difficult to digest. The purpose of this contribution is to provide an accessible account of the main ideas and results of kernel-based regularisation methods for system identification. The focus is to assess the impact of these new techniques on the field and traditional paradigms.

KEYWORDS:

• System identification
• linear regressions
• bias-variance tradeoff
• regularisation
• kernel methods

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Lennart Ljung  http://orcid.org/0000-0003-4881-8955

Additional information

Funding

The work has been supported by the National Natural Science Foundation of China under the contracts No. 61603379 and. No. 61773329, the Thousand Youth Talents Plan funded by the central government of China, the Shenzhen Projects Ji-20170189 and Ji-20160207 funded by the Shenzhen Science and Technology Innovation Council, the Presidents grant under contract No. PF. 01.000249 and the Startup grant under contract No. 2014.0003.23 funded by the Chinese University of Hong Kong, Shenzhen, by Shenzhen Fundamental Research Fund under project No. ZDSYS201707251409055, and by a research grant for junior researchers under contract No. 2014-5894 funded by Swedish Research Council.