Stochastic H identification: an iteratively weighted least squares algorithm

Research output: Contribution to journalConference articlepeer-review


We present a novel problem formulation and algorithm for H system identification based on a stochastic noise model and constrained model set to reduce the conservatism in deterministic noise models, and statistical inefficiency and computational complexity associated with high-order estimates. By establishing a connection between a minimax problem and a sequence of weighted least square problems, we show that the proposed stochastic, constrained problem can be solved with a computationally attractive and conceptually simple iteratively weighted least square (IWLS) identification algorithm. The IWLS procedure is based on a sequence of standard parametric weighted least square output error identification routines, where the weighting is updated via non-parametric estimation of the modeling error to asymptotically achieve the H identification criterion.

Original languageEnglish (US)
Pages (from-to)3374-3379
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - 1994
EventProceedings of the 33rd IEEE Conference on Decision and Control. Part 1 (of 4) - Lake Buena Vista, FL, USA
Duration: Dec 14 1994Dec 16 1994

ASJC Scopus subject areas

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


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