Abstract
This study examines and analyses the use of a new recurrent neural network model: Jordan Pi-Sigma Network (JPSN) as a forecasting tool. JPSN’s ability to predict future trends of temperature was tested and compared to that of Multilayer Perceptron (MLP) and the standard Pi-Sigma Neural Network (PSNN); trained with the standard gradient descent algorithm. A set of historical temperature measurement for five years from Malaysian Meteorological Department was used as input data to 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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Husaini, N.A., Ghazali, R., Mohd Nawi, N., Ismail, L.H. (2011). Jordan Pi-Sigma Neural Network for Temperature Prediction. In: Kim, Th., Adeli, H., Robles, R.J., Balitanas, M. (eds) Ubiquitous Computing and Multimedia Applications. UCMA 2011. Communications in Computer and Information Science, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20998-7_61
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DOI: https://doi.org/10.1007/978-3-642-20998-7_61
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