Computational Analysis of Optical Neural Network Models to Weather Forecasting
Neural networks have been in use in
numerous meteorological applications
including weather forecasting. They are found
to be more powerful than any traditional
expert system in the classification of
meteorological patterns, in performing pattern
classification tasks as they learn from
examples without explicitly stating the rules
and being non linear they solve complex
problems more than linear techniques. A
weather forecasting problem - rain fall
estimation has been experimented using
different neural network architectures namely
Electronic Neural Network (ENN) model and
opto-electronic neural network model. The
percentage of correctness of the rainfall
estimation of the neural network models and
the meteorological experts are compared. The
results of the ENN are compared with the
results of the opto-electronic neural network
for the estimation of rainfall.
Keywords: Back propagation, convergence,
neural network, opto-electronic neural
network, rainfall estimation
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