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0899-7667
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1530-888X
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Neural Computation

September 1993, Vol. 5, No. 5, Pages 795-811
(doi: 10.1162/neco.1993.5.5.795)
© 1993 Massachusetts Institute of Technology
Recurrent and Feedforward Polynomial Modeling of Coupled Time Series
Article PDF (829.65 KB)
Abstract

We present two methods for the prediction of coupled time series. The first one is based on modeling the series by a dynamic system with a polynomial format. This method can be formulated in terms of learning in a recurrent network, for which we give a computationally effective algorithm. The second method is a purely feedforward σ-π network procedure whose architecture derives from the recurrence relations for the derivatives of the trajectories of a Ricatti format dynamic system. It can also be used for the modeling of discrete series in terms of nonlinear mappings. Both methods have been tested successfully against chaotic series.