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

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
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id my-usm-ep.42853
record_format uketd_dc
spelling my-usm-ep.428532019-04-12T05:26:53Z Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression 2010-05 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 http://eprints.usm.my/42853/ http://eprints.usm.my/42853/1/YAP_KEEM_SIAH.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuteraan 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
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
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
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
granting_institution Universiti Sains Malaysia
granting_department Pusat Pengajian Kejuteraan Elektrik & Elektronik
publishDate 2010
url http://eprints.usm.my/42853/1/YAP_KEEM_SIAH.pdf
_version_ 1747821114642399232

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