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Modeling double scroll time series

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2 Author(s)
A. Dimitriadis ; Dept. of Linguistics, Pennsylvania Univ., Philadelphia, PA, USA ; A. M. Fraser

The ubiquity of strange attractors in nature suggests that nonlinear modeling techniques can improve performance in some signal processing applications. The authors introduce mixed state Markov models (MSMMs), a refinement of hidden filter HMMs, and apply both to a synthetic double scroll time series. Forecasts by HFHMMs diverge after a few steps. Using ad hoc procedures, forecasts by MSMMs, even models generated by crude methods without iterative optimization, can be made more stable

Published in:

IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing  (Volume:40 ,  Issue: 10 )