Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression

This thesis is concerned with the development of novel neural network models for tackling pattern classification, rule extraction, and data regression problems. The research focuses on one of the advanced features of neural networks, i.e., the incremental learning ability. This ability relates to co...

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Main Author:	Yap , Keem Siah
Format:	Thesis
Language:	English
Published:	2010
Subjects:	TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Online Access:	http://eprints.usm.my/42853/1/YAP_KEEM_SIAH.pdf http://eprints.usm.my/42853/
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Institution:	Universiti Sains Malaysia
Language:	English

id	my.usm.eprints.42853
record_format	eprints
spelling	my.usm.eprints.42853 http://eprints.usm.my/42853/ Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression Yap , Keem Siah TK1-9971 Electrical engineering. Electronics. Nuclear engineering This thesis is concerned with the development of novel neural network models for tackling pattern classification, rule extraction, and data regression problems. The research focuses on one of the advanced features of neural networks, i.e., the incremental learning ability. This ability relates to continuous learning of new knowledge without disturbing the existing knowledge base and without re-iterating through the training samples. The Adaptive Resonance Theory (ART) and Generalized Regression Neural Network (GRNN) models are employed as the backbone in this research. 2010-05 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/42853/1/YAP_KEEM_SIAH.pdf Yap , Keem Siah (2010) Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression. PhD thesis, Universiti Sains Malaysia.
institution	Universiti Sains Malaysia
building	Hamzah Sendut Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Sains Malaysia
content_source	USM Institutional Repository
url_provider	http://eprints.usm.my/
language	English
topic	TK1-9971 Electrical engineering. Electronics. Nuclear engineering
spellingShingle	TK1-9971 Electrical engineering. Electronics. Nuclear engineering Yap , Keem Siah Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
description	This thesis is concerned with the development of novel neural network models for tackling pattern classification, rule extraction, and data regression problems. The research focuses on one of the advanced features of neural networks, i.e., the incremental learning ability. This ability relates to continuous learning of new knowledge without disturbing the existing knowledge base and without re-iterating through the training samples. The Adaptive Resonance Theory (ART) and Generalized Regression Neural Network (GRNN) models are employed as the backbone in this research.
format	Thesis
author	Yap , Keem Siah
author_facet	Yap , Keem Siah
author_sort	Yap , Keem Siah
title	Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
title_short	Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
title_full	Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
title_fullStr	Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
title_full_unstemmed	Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
title_sort	novel art-based neural network models for pattern classification, rule extraction and data regression
publishDate	2010
url	http://eprints.usm.my/42853/1/YAP_KEEM_SIAH.pdf http://eprints.usm.my/42853/
_version_	1643710595446341632

Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression

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