New results for Hammerstein system identification

Sundeep Rangan, Greg Wolodkin, Kameshwar Poolla

Research output: Contribution to journalConference articlepeer-review


A novel approach is presented for the analysis and design of identification algorithms for Hammerstein models, which consist of a static nonlinearity followed by an LTI system. We examine two identification problems. In the first problem, the system is excited with white noise and the LTI system is FIR, and we find a simple explicit solution for the optimal parameter estimate and show that for sufficiently large data lengths a standard iterative technique globally converges to this optimal value. In the second problem, the LTI system is given in state-space form and we show that standard state-space algorithms can be easily modified to identify Hammerstein models.

Original languageEnglish (US)
Pages (from-to)697-702
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - 1995
EventProceedings of the 1995 34th IEEE Conference on Decision and Control. Part 1 (of 4) - New Orleans, LA, USA
Duration: Dec 13 1995Dec 15 1995

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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