@inproceedings{d2bcaec479ef450ba3e3509f72eb657a,
title = "Nonlinear time series prediction with a discrete-time recurrent neural network model",
abstract = "Summary form only given. The application of a discrete-time recurrent neural network model to signal processing and time series prediction was discussed. This network constitutes a black box model for input-output nonlinear system identification. Two samples were considered, which consisted of predicting a deterministic time series generated by the Mackey-Glass equation and a stochastic non-Gaussian time series. In the deterministic case, a 9-neuron network converged to a solution with prediction error comparable to that of feedforward networks, with faster learning than backpropagation, and absolutely no windowing or prior knowledge about the time series. In the stochastic case, a 25-neuron network was trained and converged to a solution close to the conditional mean (the optimal solution) also with no prior information or ad hoc assumptions. Ongoing research on the relation of neural networks to the Volterra-Wiener theory of nonlinear systems was also discussed.",
author = "Hakim, {N. Z.} and Kaufman, {J. J.} and G. Cerf and Meadows, {H. E.}",
year = "1992",
language = "English",
isbn = "0780301641",
series = "Proceedings. IJCNN - International Joint Conference on Neural Networks",
publisher = "Publ by IEEE",
pages = "900",
editor = "Anon",
booktitle = "Proceedings. IJCNN - International Joint Conference on Neural Networks",
note = "International Joint Conference on Neural Networks - IJCNN-91-Seattle ; Conference date: 08-07-1991 Through 12-07-1991",
}