Mixed transfer function neural networks for generalization and knowledge extraction
Data(s) |
01/01/2006
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Resumo |
This thesis develops a novel framework of nonlinear modelling to adaptively fit the complexity of the model to the problem domain resulting in a better modelling capability and a straightforward knowledge acquisition. The developed framework also permits increased comprehensibility and user acceptability of modelling results. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Deakin University, Faculty of Science and Technology, School of Engineering and Information Technology |
Palavras-Chave | #Neural networks (Computer science) #Computer simulation #Knowledge acquisition (Expert systems) |
Tipo |
Thesis |