Mixed transfer function neural networks for generalization and knowledge extraction

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.