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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Bibliographic Details
Main Author: Yap , Keem Siah
Format: Thesis
Language:English
Published: 2010
Subjects:
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
Description
Summary: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.