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

Yap , Keem Siah (2010) Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression. PhD thesis, Universiti Sains Malaysia.

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Abstract

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.

Item Type:	Thesis (PhD)
Subjects:	T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Divisions:	Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Thesis
Depositing User:	ASM Ab Shukor Mustapa
Date Deposited:	12 Nov 2018 02:29
Last Modified:	12 Apr 2019 05:26
URI:	http://eprints.usm.my/id/eprint/42853

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