Pi-Sigma neural network for temperature forecasting in Batu Pahat
In this study, two artificial neural network (ANN) models, a Pi-Sigma Neural Network (PSNN) and a three-layer multilayer perceptron (MLP), are applied for temperature forecasting. PSNN is use to overcome the limitation of widely used MLP, which can easily get stuck into local minima and prone to ove...
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| Main Authors: | , , , |
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| Format: | Book Section |
| Published: |
Springer-Verlag Berlin Heidelberg
2011
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/3042/ |
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| Summary: | In this study, two artificial neural network (ANN) models, a Pi-Sigma Neural Network (PSNN) and a three-layer multilayer perceptron (MLP), are applied for temperature forecasting. PSNN is use to overcome the limitation of widely used MLP, which can easily get stuck into local minima and prone to overfitting. Therefore, good generalization may not be obtained. The models were trained with backpropagation algorithm on historical temperature data of Batu Pahat region. Through 810 experiments, we found that PSNN performs considerably better results compared to MLP for daily temperature forecasting and can be suitably adapted to forecasts a particular region using the historical data over larger geographical areas. |
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