Recurrent and Feedforward Polynomial Modeling of Coupled Time Series
- 1 September 1993
- journal article
- Published by MIT Press in Neural Computation
- Vol. 5 (5) , 795-811
- https://doi.org/10.1162/neco.1993.5.5.795
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.Keywords
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