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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| 格式: | Thesis |
| 語言: | English |
| 出版: |
2010
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| 主題: | |
| 在線閱讀: | http://eprints.usm.my/42853/1/YAP_KEEM_SIAH.pdf |
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| 總結: | 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
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