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The Identification and Estimation of Nonlinear Stochastic Systems

  • Peter Young
Chapter

Abstract

This chapter describes what might be called the system theorist’s approach to understanding dynamics of nonlinear stochastic systems. The method uses so-called state-dependent parameters, and is able to handle non-stationarity, as long as the state-dependent parameters vary slowly compared to the significant dynamics. One of the main points made here is that most realistic systems have time-varying inputs which can be measured; models must take this into account, and indeed modeling often becomes easier rather than harder when this is done. We describe the methods used, based on recursive fixed-interval smoothing, and present applications to some realistic problems.

Keywords

Random Walk Model Nonlinear Stochastic System White Noise Input Generalize Random Walk Squid Data 
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 Science+Business Media New York 2001

Authors and Affiliations

  • Peter Young

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