Title :
Prediction with recurrent networks
Author :
Wulff, Niels Holger ; Hertz, John A.
Author_Institution :
CONNECT, Niels Bohr Inst., Copenhagen, Denmark
fDate :
31 Aug-2 Sep 1992
Abstract :
The authors study extrapolation of time series using recurrent neural networks. They use the real-time recurrent learning algorithm introduced by R. J. Williams and D. Zipser (1989), both in the original form for first order nets and in a form for second order nets. It is shown that both the first order and the second order nets are able to learn to simulate the Mackey-Glass series. The prediction quality of the results is comparable to that from feedforward nets
Keywords :
extrapolation; recurrent neural nets; time series; first order nets; prediction quality; real-time recurrent learning algorithm; recurrent neural networks; second order nets; Chaos; Delay; Differential equations; Extrapolation; Feedforward systems; Fractals; Polynomials; Recurrent neural networks; Sampling methods; Time measurement;
Conference_Titel :
Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop
Conference_Location :
Helsingoer
Print_ISBN :
0-7803-0557-4
DOI :
10.1109/NNSP.1992.253666
