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  1. University of Computer Studies, Yangon
  2. Conferences

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/20.500.12678/0000004441
abc0e8c0-534d-4af8-930d-bd259f1cedc0
258ae373-7e2d-4505-840d-0976745b3eaf
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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
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