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5. Deterministic convergence of an online gradient method for BP neural networks

Author

Wu, Wei, Feng, Guorui, Li, Zhengxue, and Xu, Yuesheng

Subjects
Convergence (Mathematics) -- Analysis, Neural networks -- Analysis, Neural network, Business, Computers, Electronics, Electronics and electrical industries
Abstract

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. Index Terms--Online gradient methods, backward propagation (BP) neural networks, convergence.

Published
2005

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