Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin

Artificial neural network (ANN) is one of the most prominent universal approximators, and has been implemented tremendously in forecasting arena. The aforementioned neural network forecasting models are feedforward (nonlinear autoregressive) and recurrent (nonlinear autoregressive moving average). T...

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Bibliographic Details
Main Author:	Ahmad Kamaruddin, Saadi
Format:	Thesis
Language:	English
Published:	2018
Subjects:	Malaysia
Online Access:	https://ir.uitm.edu.my/id/eprint/96071/1/96071.pdf
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id	my-uitm-ir.96071
record_format	uketd_dc
spelling	my-uitm-ir.960712024-05-29T07:39:51Z Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin 2018 Ahmad Kamaruddin, Saadi Malaysia Artificial neural network (ANN) is one of the most prominent universal approximators, and has been implemented tremendously in forecasting arena. The aforementioned neural network forecasting models are feedforward (nonlinear autoregressive) and recurrent (nonlinear autoregressive moving average). Theoretically, the most common algorithm to train the network is the backpropagation (BP) algorithm which is based on the minimization of the ordinary least squares (LS) estimator in terms of mean squared error (MSE). However, this algorithm is not totally robust in the presence of outliers that usually exist in the routine time series data, and this may cause false prediction of future values. 2018 Thesis https://ir.uitm.edu.my/id/eprint/96071/ https://ir.uitm.edu.my/id/eprint/96071/1/96071.pdf text en public phd doctoral Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Md. Ghani, Nor Azura
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Md. Ghani, Nor Azura
topic	Malaysia
spellingShingle	Malaysia Ahmad Kamaruddin, Saadi Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin
description	Artificial neural network (ANN) is one of the most prominent universal approximators, and has been implemented tremendously in forecasting arena. The aforementioned neural network forecasting models are feedforward (nonlinear autoregressive) and recurrent (nonlinear autoregressive moving average). Theoretically, the most common algorithm to train the network is the backpropagation (BP) algorithm which is based on the minimization of the ordinary least squares (LS) estimator in terms of mean squared error (MSE). However, this algorithm is not totally robust in the presence of outliers that usually exist in the routine time series data, and this may cause false prediction of future values.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ahmad Kamaruddin, Saadi
author_facet	Ahmad Kamaruddin, Saadi
author_sort	Ahmad Kamaruddin, Saadi
title	Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin
title_short	Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin
title_full	Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin
title_fullStr	Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin
title_full_unstemmed	Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin
title_sort	modified artificial neural network (ann) models for malaysian construction costs indices (mcci) data / saadi ahmad kamaruddin
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2018
url	https://ir.uitm.edu.my/id/eprint/96071/1/96071.pdf
_version_	1804889980015738880

Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin

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