Dynamical recurrent neural networks - towards environmental time series prediction.

Details Dynamical recurrent neural networks - towards environmental time series prediction. Dynamical recurrent neural networks - towards environmental time series prediction.

Jun 1995

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1995ijns....6..145a&link_type=abstract

International Journa l of Neural Syst., Vol. 6, No. 2, p. 145 - 170

Mathematics

Logic

Vlt: Site Testing

Scientific paper

Dynamical recurrent neural networks (DRNN) are a class of fully recurrent networks obtained by modeling synapses as autoregressive filters. By virtue of their internal dynamic, these networks approximate the underlying law governing the time series by a system of nonlinear difference equations of internal variables. They therefore provide history-sensitive forecasts without having to be explicitly fed with external memory. The model is trained by a local and recursive error propagation algorithm called temporal - recurrent - backpropagation. The efficiency of the procedure benefits from the exponential decay of the gradient terms backpropagated through the adjoint network. We assess the predictive ability of the DRNN model with meteorological and astronomical time series recorded around the candidate observation sites for the future VLT telescope. The hope is that reliable environmental forecasts provided with the model will allow the modern telescopes to be preset, a few hours in advance, in the most suited instrumental mode.

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