The Jordan Pi-Sigma neural network for temperature prediction

Husaini, Noor Aida (2012) The Jordan Pi-Sigma neural network for temperature prediction. Masters thesis, Universiti Tun Hussein Onn Malaysia.



In recent years, various temperature forecasting models have been proposed, which broadly can be classified into physically-based approaches and statistically-based approaches. Hitherto, those approaches involve sophisticated mathematical models to justify the use of empirical rules which make them less desirable for some applications. Therefore, in this respect, Neural Networks (NN) have been successfully applied and with no doubt, they provide the ability and potentials to predict the temperature events. However, the ordinary NN adopts computationally intensive training algorithms and can easily get trapped into local minima. To overcome such drawbacks in ordinary NN, this research focuses on using a Higher Order Neural Network (HONN). Pi-Sigma Neural Network (PSNN) which lies within this area, is able to maintain the high learning capabilities of HONN. The use of PSNN itself for temperature forecasting is preferably utilisable just yet. Notwithstanding, this study disposed towards an idea to develop a new network model called a Jordan Pi-Sigma Neural Network (JPSN) to overcome the drawbacks of ordinary NN, whilst taking the advantages of PSNN. JPSN, a network model with a single layer of tuneable weights with a recurrent term added in the network, is trained using the standard backpropagation gradient descent algorithm. The network was used to learn a set of historical temperature data of Batu Pahat region for five years (2005-2009), obtained from Malaysian Meteorological Department (MMD). JPSN’s ability to predict the future trends of temperature was tested and compared to that of Multilayer Perceptron (MLP) and the standard PSNN. Simulation results proved that JPSN’s forecast comparatively superior to MLP and PSNN models, with lower prediction error, thus revealing a great potential for JPSN as an alternative mechanism to both PSNN and ordinary NN in predicting the temperature measurement for one-step-ahead.

Item Type:Thesis (Masters)
Subjects:Q Science > QA Mathematics > QA76 Computer software
Q Science > QC Physics
ID Code:2929
Deposited By:Normajihan Abd. Rahman
Deposited On:07 Nov 2012 16:10
Last Modified:07 Nov 2012 16:10

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