Nuclear norm subspace identification method for Hammerstein system identification

Mingxiang Dai, Jingxin Zhang, Ying He, Xinmin Yang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


In this paper, in order to solve the dimension problem in over-parameterized method (OPM) and the rank constraint problem in subspace identification method (SIM), The nuclear norm subspace identification method (N2SID) is proposed with a combination of nuclear norm minimization (NNM) and least-parameterized method (LPM). NNM is a heuristic convex relaxation of the rank
minimization, and preprocesses the measured data to obtain an optimized Hankel matrix with lower rank for subspace identification. In addition, NNM descends the nonzero singular values of Hankel matrix caused by extra noise near to zero to improve the order identification of SIM. LPM takes into account the dimension problem in the conventional OPM and identifies the Hammerstein system with the least estimation parameters. N2SID benefits the advantages of both NNM and LPM to improve the identification of Hammerstein system. Furthermore, a numerical example is presented to illustrate the improvement on Hammerstein system identification by N2SID through comparing with LPM and OPM.
Original languageEnglish
Title of host publication2014 11th World Congress on Intelligent Control and Automation (WCICA)
EditorsHong Wang
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2334 - 2339
Number of pages6
ISBN (Print)9781479958269
Publication statusPublished - 2015
EventWorld Congress on Intelligent Control and Automation 2014 - Shenyang, China
Duration: 29 Jun 20144 Jul 2014
Conference number: 11th


ConferenceWorld Congress on Intelligent Control and Automation 2014
Abbreviated titleWCICA 2014


  • Hammerstein System
  • Least-parameterized method
  • Nuclear Norm Minimization
  • Subspace Identification Method

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