The Jordan Pi-Sigma neural network for temperature prediction

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 th...

Full description

Saved in:

Bibliographic Details
Main Author:	Husaini, Noor Aida
Format:	Thesis
Language:	English
Published:	2012
Subjects:	QA76 Computer software QA71-90 Instruments and machines
Online Access:	http://eprints.uthm.edu.my/2330/1/24p%20NOOR%20AIDA%20HUSAINI.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uthm-ep.2330
record_format	uketd_dc
spelling	my-uthm-ep.23302022-02-03T01:48:35Z The Jordan Pi-Sigma neural network for temperature prediction 2012-06 Husaini, Noor Aida QA76 Computer software QA71-90 Instruments and machines 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. 2012-06 Thesis http://eprints.uthm.edu.my/2330/ http://eprints.uthm.edu.my/2330/1/24p%20NOOR%20AIDA%20HUSAINI.pdf text en public mphil masters Universiti Tun Hussein Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English
topic	QA76 Computer software QA71-90 Instruments and machines
spellingShingle	QA76 Computer software QA71-90 Instruments and machines Husaini, Noor Aida The Jordan Pi-Sigma neural network for temperature prediction
description	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.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Husaini, Noor Aida
author_facet	Husaini, Noor Aida
author_sort	Husaini, Noor Aida
title	The Jordan Pi-Sigma neural network for temperature prediction
title_short	The Jordan Pi-Sigma neural network for temperature prediction
title_full	The Jordan Pi-Sigma neural network for temperature prediction
title_fullStr	The Jordan Pi-Sigma neural network for temperature prediction
title_full_unstemmed	The Jordan Pi-Sigma neural network for temperature prediction
title_sort	jordan pi-sigma neural network for temperature prediction
granting_institution	Universiti Tun Hussein Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2012
url	http://eprints.uthm.edu.my/2330/1/24p%20NOOR%20AIDA%20HUSAINI.pdf
_version_	1747830939975680000

The Jordan Pi-Sigma neural network for temperature prediction

Similar Items