Convergence of Online Gradient Method with Penalty for BP Neural Networks

Online gradient method has been widely used as a learning algorithm for training feedforward neural networks. Penalty is often introduced into the training procedure to improve the generalization performance and to decrease the magnitude of network weights. In this paper,some weight boundedness and deterministic convergence theorems are proved for the online gradient method with penalty for BP neural network with a hidden layer,assuming that the training samples are supplied with the network in a fixed orde...