Conjugate gradient descent learned ANN for Indian summer monsoon rainfall and efficiency assessment through Shannon-Fano coding
| Autor: | Goutami Chattopadhyay, Surajit Chattopadhyay |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Computer Science::Neural and Evolutionary Computation 0208 environmental biotechnology 02 engineering and technology Monsoon 01 natural sciences Backpropagation 020801 environmental engineering Geophysics Shannon–Fano coding Indian summer monsoon rainfall Space and Planetary Science Algorithmic efficiency Conjugate gradient method Statistics Physics::Atmospheric and Oceanic Physics 0105 earth and related environmental sciences Mathematics Coding (social sciences) |
| Zdroj: | Journal of Atmospheric and Solar-Terrestrial Physics. 179:202-205 |
| ISSN: | 1364-6826 |
| Popis: | Work reported in the present paper demonstrates a neurocomputing based predictive model for the average rainfall in India during the season of summer monsoon. Backpropagation method with Conjugate Gradient Descent algorithm has been implemented to develop the neurocomputing model. After three runs of the model, it is found that a high prediction yield is available. Finally, Shannon-Fano coding has been implemented and the coding efficiency has been measured by dividing the error percentage of prediction into various classes. The efficiency of Conjugate Gradient Descent algorithm for multilayer ANN has been finally established through Shannon-Fano coding. |
| Databáze: | OpenAIRE |
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