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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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WHO_WE_ARE WHAT_WE_DO TECHNOLOGY General Synaptics Aerospace_&_Defence_Machine_Learning_Company VAT NUMBER:_IT0267070992 REA NUMBER: GE-503104 Email:_luca.marchese@synaptics.org |
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Contacts_and_Networks
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