An identification algorithm for parallel Wiener-Hammerstein systems

M. Schoukens, G. Vandersteen, Y. Rolain

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

14 Citations (Scopus)


Block-oriented nonlinear models such as Wiener and Hammerstein models have the advantage that they are quite simple to understand and easy to use. Hammerstein and Wiener models can be extended to models containing extra blocks in a series connection such as Wiener-Hammerstein models. To further increase the modeling power of block-oriented models a parallel connection of Wiener-Hammerstein branches is considered. T his paper presents a parametric identification algorithm for parallel Wiener-Hammerstein systems in discrete time starting from input-output data only. First, the overall dynamics of the system are estimated in least squares sense at different operating points of the system. Second, these dynamics are decomposed over the parallel branches, and partitioned into the front and back linear time invariant (LTI) blocks, giving an estimate of the LTI blocks. Finally, the static nonlinearities are estimated using a linear least squares estimator.

Original languageEnglish
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Print)9781467357173
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event52nd IEEE Conference on Decision and Control (CDC 2013) - Florence, Italy
Duration: 10 Dec 201313 Dec 2013
Conference number: 52


Conference52nd IEEE Conference on Decision and Control (CDC 2013)
Abbreviated titleCDC 2013


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