Jordan Pi-sigma neural network for temperature prediction
This study examines and analyses the use of a new recurrent neural network model: Jordan Pi-Sigma Network (JPSN) as a forecasting tools. JPSN's ability to predict future trends of temperature was tested and compared to that of Multilayer Perception (MLP) and the standard Pi-Sigma Neural Network...
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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/3040/ |
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| Summary: | This study examines and analyses the use of a new recurrent neural network model: Jordan Pi-Sigma Network (JPSN) as a forecasting tools. JPSN's ability to predict future trends of temperature was tested and compared to that of Multilayer Perception (MLP) and the standard Pi-Sigma Neural Network (PSNN); trained with the standard gradient descent algorithm. A set of historical temperature for five years from Malaysian Meteorological Department was used as input data train the networks for the next-day prediction. Simulation results show that JPSN forecast comparatively superior to MLP and PSNN models, with lower prediction error, thus revealing a great potential in predicting the temperature measurement. |
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