Particle Filters for Prediction of Chaos
نویسندگان
چکیده
The use of particle filters for the prediction of time series arising from chaotic dynamical systems is explored. The specific dynamical systems considered are variations of the logistical map with an unknown parameter. This parameter is in the chaotic regime for these dynamical systems. The systems considered have both observation and process noise. The prediction algorithms studied are variations of particle filters which include a roughening technique. Cramer-Rao bounds for the prediction algorithm are developed and compared with Monte-Carlo simulations.
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تاریخ انتشار 2003