Dual systems identification methods based on Koopman operator theory

Alexandre Mauroy, Jorge Goncalves

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution


We report our recent developments on Koopman operator lifting techniques for system identification and parameter estimation. We present two methods, which are based on the key idea of identifying the Koopman operator in a lifted space of observables, but rely on two different finite-dimensional approximations of the Koopman operator. The first method is a parametric technique which reconstructs the vector field using a dictionary of library functions. The second method can be seen as a dual approach and provides estimates of the vector field at the data points. We compare the performances of these two methods and consider large
dimensional systems. Theoretical convergence results are also provided.
Original languageEnglish
Title of host publicationProceedings of the SICE Conference
Number of pages4
Publication statusPublished - Sep 2018


  • Nonlinear systems identification
  • parameter estimation
  • Koopman operator
  • lifting techniques


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