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Universal output prediction and nonparametric regression for arbitrary data

  • S. R. Kulkarni
  • S. E. Posner
Part C Modeling, Identification And Estimation
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 241)

Abstract

We construct a class of elementary nonparametric output predictors of an unknown discrete-time nonlinear fading memory system. Our algorithms predict asymptotically well for every bounded input sequence, every disturbance sequence in certain classes, and every linear or nonlinear system that is continuous and asymptotically time-invariant, causal, and with fading memory. The predictor is based on k n -nearest neighbor estimators from nonparametric statistics. It uses only previous input and noisy output data of the system without any knowledge of the structure of the unknown system, the bounds on the input, or the properties of noise. Under additional smoothness conditions we provide rates of convergence for the time-average errors of our scheme. Finally, we apply our results to the special case of stable LTI systems.

Keywords

Prediction Error Input Sequence Near Neighbor Nonparametric Regression Input String 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 1999

Authors and Affiliations

  • S. R. Kulkarni
    • 1
  • S. E. Posner
    • 2
  1. 1.Department of Electrical EngineeringPrinceton UniversityPrinceton
  2. 2.ING Baring Securities, Inc.New York

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