Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction

(Research into the field of artificial neural networks (ANN) is fast gaining interest in recent years, due to the fact that it is fast becoming a popular tool of choice in prediction of time series trends. This recent surge in its popularity can be attributed to the fact that ANN, especially a multi...

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Main Author:	Tan, James Yiaw Beng
Format:	Thesis
Language:	English
Published:	2012
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	http://ir.unimas.my/id/eprint/14395/3/Optimization%20of%20the%20Hidden%20Layer%20of%20A%20Multilayer%20Perception%20with%20Backpropagation%20%28BP%29%20Network%20Using%20Hybrid%20K-Means-Greedy%20Algorithm%20%28KGA%29%20for%20Time%20Series%20Prediction%20%28Fulltext%29.pdf
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spelling	my-unimas-ir.143952023-05-03T09:02:52Z Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction 2012 Tan, James Yiaw Beng TA Engineering (General). Civil engineering (General) (Research into the field of artificial neural networks (ANN) is fast gaining interest in recent years, due to the fact that it is fast becoming a popular tool of choice in prediction of time series trends. This recent surge in its popularity can be attributed to the fact that ANN, especially a multilayer perceptron with backpropagation (BP) network that has the optimal number of neurons in its hidden layer would be able to predict with better accuracy unknown values of a time series that it is trained with, compared to other methods implemented to predict the same time series The drawback of using BP networks in time series prediction is that it is difficult and time-consuming to find the optimal number of neurons in its hidden layer to minimize the prediction error. We propose a model known as K-means-Greedy Algorithm (KGA) model in this research to overcome this serious drawback of the BP network. The proposed KGA model combines greedy algorithm withk-means++ clustering in this research to assist users in automating the finding of the optimal number of new-ons inside the hidden layer of the BP network. The evaluation results the proposed KGA model using several time series, namely the sunspot data, the Mackey-Glass time series, and electrical load forecasting using data from several econometric factors, as well as historical electricity demand data, show that the proposed KGA model is eflective in finding the optimal number ofneurons for the hidden layer of a BP network that is used to perform time series prediction. Universiti Malaysia Sarawak (UNIMAS) 2012 Thesis http://ir.unimas.my/id/eprint/14395/ http://ir.unimas.my/id/eprint/14395/3/Optimization%20of%20the%20Hidden%20Layer%20of%20A%20Multilayer%20Perception%20with%20Backpropagation%20%28BP%29%20Network%20Using%20Hybrid%20K-Means-Greedy%20Algorithm%20%28KGA%29%20for%20Time%20Series%20Prediction%20%28Fulltext%29.pdf text en validuser masters Universiti Malaysia Sarawak Faculty of Engineering
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Tan, James Yiaw Beng Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction
description	(Research into the field of artificial neural networks (ANN) is fast gaining interest in recent years, due to the fact that it is fast becoming a popular tool of choice in prediction of time series trends. This recent surge in its popularity can be attributed to the fact that ANN, especially a multilayer perceptron with backpropagation (BP) network that has the optimal number of neurons in its hidden layer would be able to predict with better accuracy unknown values of a time series that it is trained with, compared to other methods implemented to predict the same time series The drawback of using BP networks in time series prediction is that it is difficult and time-consuming to find the optimal number of neurons in its hidden layer to minimize the prediction error. We propose a model known as K-means-Greedy Algorithm (KGA) model in this research to overcome this serious drawback of the BP network. The proposed KGA model combines greedy algorithm withk-means++ clustering in this research to assist users in automating the finding of the optimal number of new-ons inside the hidden layer of the BP network. The evaluation results the proposed KGA model using several time series, namely the sunspot data, the Mackey-Glass time series, and electrical load forecasting using data from several econometric factors, as well as historical electricity demand data, show that the proposed KGA model is eflective in finding the optimal number ofneurons for the hidden layer of a BP network that is used to perform time series prediction.
format	Thesis
qualification_level	Master's degree
author	Tan, James Yiaw Beng
author_facet	Tan, James Yiaw Beng
author_sort	Tan, James Yiaw Beng
title	Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction
title_short	Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction
title_full	Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction
title_fullStr	Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction
title_full_unstemmed	Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction
title_sort	optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction
granting_institution	Universiti Malaysia Sarawak
granting_department	Faculty of Engineering
publishDate	2012
url	http://ir.unimas.my/id/eprint/14395/3/Optimization%20of%20the%20Hidden%20Layer%20of%20A%20Multilayer%20Perception%20with%20Backpropagation%20%28BP%29%20Network%20Using%20Hybrid%20K-Means-Greedy%20Algorithm%20%28KGA%29%20for%20Time%20Series%20Prediction%20%28Fulltext%29.pdf
_version_	1783728162762391552

Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction

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