Mixed transfer function neural networks for knowledge acquistition
Khan, M. Imad, Frayman, Yakov and Nahavandi, Saeid 2009, Mixed transfer function neural networks for knowledge acquistition, in ICIT 2009 : IEEE International Conference on Industrial Tecnology : Future technology in service of regional industry, IEEE, Piscataway, N. J., pp. 1-6.
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ICIT 2009 : IEEE International Conference on Industrial Tecnology : Future technology in service of regional industry
International Conference on Industrial Technology
Place of publication
Piscataway, N. J.
Modeling helps to understand and predict the outcome of complex systems. Inductive modeling methodologies are beneficial for modeling the systems where the uncertainties involved in the system do not permit to obtain an accurate physical model. However inductive models, like artificial neural networks (ANNs), may suffer from a few drawbacks involving over-fitting and the difficulty to easily understand the model itself. This can result in user reluctance to accept the model or even complete rejection of the modeling results. Thus, it becomes highly desirable to make such inductive models more comprehensible and to automatically determine the model complexity to avoid over-fitting. In this paper, we propose a novel type of ANN, a mixed transfer function artificial neural network (MTFANN), which aims to improve the complexity fitting and comprehensibility of the most popular type of ANN (MLP - a Multilayer Perceptron).
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