Convergence of Online Gradient Method for Pi-sigma Neural Networks with Inner-penalty Terms
نویسنده
چکیده
This paper investigates an online gradient method with innerpenalty for a novel feed forward network it is called pi-sigma network. This network utilizes product cells as the output units to indirectly incorporate the capabilities of higherorder networks while using a fewer number of weights and processing units. Penalty term methods have been widely used to improve the generalization performance of feed forward neural networks and to control the magnitude of the network weights. The monotonicity of the error function and weight boundedness with innerpenalty term and both weak and strong convergence theorems in the training iteration are proved. Keyword: Convergence, Pi-sigma Network, Online Gradient Method, Inner-penalty, Boundedness
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