Optimal transfer function neural networks

Neural networks use neurons of the same type in each layer but such architecture cannot lead to data models of optimal complexity and accuracy. Networks with architectures (number of neurons, connections and type of neurons) optimized for a given problem are described here. Each neuron may implement transfer function of different type. Complexity of such networks is controlled by statistical criteria and by adding penalty terms to the error function. Results of numerical experiments on artificial data are reported.

• Type: Conference
• Year: 2001
• Where: ESANN
• Authors: Norbert Jankowski, Wlodzislaw Duch
• Keywords: ESANN 2001, ESANN 2007, Neural Networks, Neuron, Optimal Complexity