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One of the first (2002) Very-Deep Learning applications
MeteoNet™ was one of the
first Very-Deep Learning applications on meteorology. MeteoNet™ is a software
tool that processes thousands of temperature, humidity and pressure data on a
national geographic scale (
The training of the Multilayer Perceptron
has been optimized with hybrid techniques based on EBP (Error Back
Propagation), Genetic Algorithms and Simulated Annealing. This methodology
allows the overcoming of local minima in gradient descent. Training the network
took several months with the use of accelerators based on DSP (Digital Signal
Processing) as at that time GPUs for machine learning
did not exist.
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Local temperature time series
prediction |
Meteonet™ has been
realized for Dip.Te.Ris (Dipartimento
del Territorio e delle
sue Risorse) which is now called DISTAV (Dipartimento di Scienze della Terra, dell’Ambiente e della
Vita) |
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Geographic locations temperature time series prediction |
Four DSP acceleration boards have
been used for the training process with thousands of meteorological data |
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LUCA MARCHESE Aerospace_&_Defence_Machine_Learning_Company VAT:_IT0267070992 Email:_luca.marchese@synaptics.org |
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