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Deterministic convergence of an online gradient method for BP neural networks

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4 Author(s)
Wei Wu ; Appl. Math. Dept., Dalian Univ. of Technol., China ; Guorui Feng ; Zhengxue Li ; Yuesheng Xu

Online gradient methods are widely used for training feedforward neural networks. We prove in this paper a convergence theorem for an online gradient method with variable step size for backward propagation (BP) neural networks with a hidden layer. Unlike most of the convergence results that are of probabilistic and nonmonotone nature, the convergence result that we establish here has a deterministic and monotone nature.

Published in:

IEEE Transactions on Neural Networks  (Volume:16 ,  Issue: 3 )