Fuzzy astronomical seeing nowcasts with a dynamical and recurrent connectionist network

We assess a neural-based method for fuzzy astronomical seeing prediction, based on known meteorological variables at the same time-point. This multiple regression, termed nowcasting 10, 11], 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 measurements partly made at Cerro Paranal in Chile, site of the Very Large Telecope (VLT). A fuzzy correspondence analysis is carried out to explore the internal relationships in the data. Then, a time-and space-recurrent network is used in combination with a fuzzy coding approach to capture the temporal regularities of the seeing series. Such a connectionist network is endowed with an internal dynamic by means of arbitrary recurrent time-delayed connections. The performance of the model is appraised and the results are compared with the fuzzy k-nearest neighbors method.