State-space LPV model identification using kernelized machine learning

S.Z. Rizvi, J.M. Velni, F. Abbasi, R. Tóth, N. Meskin

Research output: Contribution to journalArticleAcademicpeer-review

37 Citations (Scopus)
3 Downloads (Pure)


This paper presents a nonparametric method for identification of MIMO linear parameter-varying (LPV) models in state-space form. The states are first estimated up to a similarity transformation via a nonlinear canonical correlation analysis (CCA) operating in a reproducing kernel Hilbert space (RKHS). This enables to reconstruct a minimal-dimensional inference between past and future input, output and scheduling variables, making it possible to estimate a state sequence consistent with the data. Once the states are estimated, a least-squares support vector machine (LS-SVM)-based identification scheme is formulated, allowing to capture the dependency structure of the matrices of the estimated state-space model on the scheduling variables without requiring an explicit declaration of these often unknown dependencies; instead, it only requires the selection of nonlinear kernel functions and the tuning of the associated hyper-parameters.

Original languageEnglish
Pages (from-to)38-47
Number of pages10
Publication statusPublished - 1 Feb 2018


  • Kernels
  • Linear parameter-varying models
  • Nonparametric identification
  • Support vector machines


Dive into the research topics of 'State-space LPV model identification using kernelized machine learning'. Together they form a unique fingerprint.

Cite this