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Forecasting of Maximum and Minimum Temperature in Mandalay by Evolving Artificial Neural Networks Using Genetic Algorithms
http://hdl.handle.net/20.500.12678/0000004441
http://hdl.handle.net/20.500.12678/0000004441abc0e8c0-534d-4af8-930d-bd259f1cedc0
258ae373-7e2d-4505-840d-0976745b3eaf
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10002.pdf (505 Kb)
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Article | ||||||
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Publication | ||||||
Title | ||||||
Title | Forecasting of Maximum and Minimum Temperature in Mandalay by Evolving Artificial Neural Networks Using Genetic Algorithms | |||||
Language | en_US | |||||
Publication date | 2012-02-28 | |||||
Authors | ||||||
Wutyi, Khaing Shwe | ||||||
Description | ||||||
Forecasting of weather is very popular innowadays. But forecasting the future from theobserved past is very difficult. There are severalforecasting methods for weather data. Amongthem, evolving artificial neural networks aresuitable for weather time series forecastingbecause of their abilities to learn and adapt anew situation by recognizing new patterns inprevious data. However, ANNs present somedrawbacks such as over fitting and long timeprocessing. Using ANNs together with geneticalgorithm comes to solutions of these problems.Genetic artificial neural networks (GANNs) cangive optimal forecasting result from the observedpast. In order to provide more effective ANNs,the proposed system use cascade backpropagation instead of back propagationmethod. Weather parameters (attributes) such asrain fall (precipitation), humidity, wind force,dew point, sea level, wind direction will also beused to forecast maximum and minimumtemperature. | ||||||
Keywords | ||||||
Artificial Neural Networks (ANN), Genetic Algorithms (GA), Time Series (TS), Mean Square Error (MSE), Specific Mean Square Error (SMSE), Multi Layer Perceptron(MLP) | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/2373 | |||||
Journal articles | ||||||
Tenth International Conference On Computer Applications (ICCA 2012) | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |