Time Series Prediction Using Recurrent Neural Networks And Boosting: An Experimental Study In Pharmaceutical Product Formulation

Tesis ini berpusat pada perkembangan teknik Rangkaian Neural Buatan (ANN) dalam menyelesaikan masalah-masalah ramalan siri masa. Penyelidikan ini tertumpu kepada penggunaan·- rangkaian-rangkaian neural perulangan yang menyediakan satu kerangka yang menyeluruh bagi fonnulasi produk fannasi melalui...

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Main Author:	Goh, Wei Yee
Format:	Thesis
Language:	English
Published:	2002
Subjects:	TK1-9971 Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.usm.my/30471/1/GOHWEIYEE.pdf
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id	my-usm-ep.30471
record_format	uketd_dc
spelling	my-usm-ep.304712017-05-31T05:06:45Z Time Series Prediction Using Recurrent Neural Networks And Boosting: An Experimental Study In Pharmaceutical Product Formulation 2002-05 Goh, Wei Yee TK1-9971 Electrical engineering. Electronics. Nuclear engineering Tesis ini berpusat pada perkembangan teknik Rangkaian Neural Buatan (ANN) dalam menyelesaikan masalah-masalah ramalan siri masa. Penyelidikan ini tertumpu kepada penggunaan·- rangkaian-rangkaian neural perulangan yang menyediakan satu kerangka yang menyeluruh bagi fonnulasi produk fannasi melalui pendekatan ramalan siri masa. Khususnya, kerangka ini telah menjelajahi paradigma pembelajaran ANN dalam mengendalikan perancangan eksperimen dan analisis. Berdasarkan kepada kaedah-kaedah yang sedia ada, reka bentuk ANN yang baru dicadangkan untuk analisis siri masa di dalam proses formulasi produk fannasi. This thesis is devoted to the development of Artificial Neural Network (ANN) techniques for solving time-series prediction problems. The research is focused on the use of recurrent neural networks for devising a comprehensible framework for pharmaceutical product formulation using time series prediction approach. In particular, the framework explores the learning paradigms of ANNs for conducting the experimental design and analysis. Based upon existing methodologies, novel ANN architectures are proposed for time series analyses in the process of pharmac~utical product formulation. 2002-05 Thesis http://eprints.usm.my/30471/ http://eprints.usm.my/30471/1/GOHWEIYEE.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik & Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK1-9971 Electrical engineering Electronics Nuclear engineering
spellingShingle	TK1-9971 Electrical engineering Electronics Nuclear engineering Goh, Wei Yee Time Series Prediction Using Recurrent Neural Networks And Boosting: An Experimental Study In Pharmaceutical Product Formulation
description	Tesis ini berpusat pada perkembangan teknik Rangkaian Neural Buatan (ANN) dalam menyelesaikan masalah-masalah ramalan siri masa. Penyelidikan ini tertumpu kepada penggunaan·- rangkaian-rangkaian neural perulangan yang menyediakan satu kerangka yang menyeluruh bagi fonnulasi produk fannasi melalui pendekatan ramalan siri masa. Khususnya, kerangka ini telah menjelajahi paradigma pembelajaran ANN dalam mengendalikan perancangan eksperimen dan analisis. Berdasarkan kepada kaedah-kaedah yang sedia ada, reka bentuk ANN yang baru dicadangkan untuk analisis siri masa di dalam proses formulasi produk fannasi. This thesis is devoted to the development of Artificial Neural Network (ANN) techniques for solving time-series prediction problems. The research is focused on the use of recurrent neural networks for devising a comprehensible framework for pharmaceutical product formulation using time series prediction approach. In particular, the framework explores the learning paradigms of ANNs for conducting the experimental design and analysis. Based upon existing methodologies, novel ANN architectures are proposed for time series analyses in the process of pharmac~utical product formulation.
format	Thesis
qualification_level	Master's degree
author	Goh, Wei Yee
author_facet	Goh, Wei Yee
author_sort	Goh, Wei Yee
title	Time Series Prediction Using Recurrent Neural Networks And Boosting: An Experimental Study In Pharmaceutical Product Formulation
title_short	Time Series Prediction Using Recurrent Neural Networks And Boosting: An Experimental Study In Pharmaceutical Product Formulation
title_full	Time Series Prediction Using Recurrent Neural Networks And Boosting: An Experimental Study In Pharmaceutical Product Formulation
title_fullStr	Time Series Prediction Using Recurrent Neural Networks And Boosting: An Experimental Study In Pharmaceutical Product Formulation
title_full_unstemmed	Time Series Prediction Using Recurrent Neural Networks And Boosting: An Experimental Study In Pharmaceutical Product Formulation
title_sort	time series prediction using recurrent neural networks and boosting: an experimental study in pharmaceutical product formulation
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik & Elektronik
publishDate	2002
url	http://eprints.usm.my/30471/1/GOHWEIYEE.pdf
_version_	1747820342810771456

Time Series Prediction Using Recurrent Neural Networks And Boosting: An Experimental Study In Pharmaceutical Product Formulation

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