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This paper addresses the use of dynamical recurrent neural networks (DRNN) for time series prediction and modeling of small dynamical systems. Since the recurrent synapses are represented by finite impulse response (FIR) filters, DRNN are state-based connectionist models in which all hidden units act as state variables of a dynamical system. The model is trained with temporal recurrent backprop (TRBP),...
We assess a neural-based method for fuzzy astronomical seeing prediction, based on known meteorological variables at the same time-point. This multiple regression (or nowcasting ) will allow the modern telescopes to be preset, a few hours in advance, in the most suited instrumental mode. The data used are extensive meteorological and seeing (observing quality) measurements partly made at Cerro...
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