The effect of network parameters on pi-sigma neural network for temperature forecasting
In this paper, we present the effect of network parameters to forecast temperature of a suburban area in Batu Pahat, Johore. The common ways of predicting the temperature using Neural Network has been applied for most meteorological parameters. However, researchers frequently neglected the networ...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/3043/ |
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| Summary: | In this paper, we present the effect of network parameters to forecast temperature of a suburban
area in Batu Pahat, Johore. The common ways of predicting the temperature using Neural Network
has been applied for most meteorological parameters. However, researchers frequently neglected
the network parameters which might affect the Neural Network’s performance. Therefore, this
study tends to explore the effect of network parameters by using Pi-Sigma Neural Network
(PSNN) with backpropagation algorithm. The network’s performance is evaluated using the
historical dataset of temperature in Batu Pahat for one-step-ahead and benchmarked against
Multilayer Perceptron (MLP) for comparison. We found out that, network parameters have
significantly affected the performance of PSNN for temperature forecasting. Towards the end of
this paper, we concluded the best forecasting model to predict the temperature based on the
comparison of our study. |
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