Universal prediction of nonlinear systems

S. R. Kulkarni, S. E. Posner

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


We construct a class of elementary nonparametric output predictors of an unknown nonlinear system. Our algorithms predict asymptotically well for every bounded input sequence, every disturbance sequence in certain classes, and every nonlinear system that is bounded, continuous, and asymptotically time-invariant, causal, with decaying memory. The predictor uses only previous input and noisy output data of the system without any knowledge of the structure of the nonlinear system. Under additional smoothness conditions we provide rates of convergence for our scheme. Finally, we apply our results to the special case of stable LTI systems.

Original languageEnglish (US)
Pages (from-to)4024-4029
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

All Science Journal Classification (ASJC) codes

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


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