Incremental least squares methods and the extended Kalman filter

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


In this paper we propose and analyze nonlinear least squares methods, which process the data incrementally, one data block at a time. Such methods are well suited for large data sets and real time operation, and have received much attention in the context of neural network training problems. We focus on the Extended Kalman Filter, which may be viewed as an incremental version of the Gauss-Newton method. We provide a nonstochastic analysis of its convergence properties, and we discuss variants aimed at accelerating its convergence.

Original languageEnglish (US)
Pages (from-to)1211-1214
Number of pages4
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - 1994
Externally publishedYes
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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