Adjoint-functions and temporal learning algorithms in neural networks
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Elementary Numerical Analysis: An Algorithmic Approach
Elementary Numerical Analysis: An Algorithmic Approach
Learning state space trajectories in recurrent neural networks
Neural Computation
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
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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.