Deterministic convergence of an online gradient method for BP neural networks.
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.
Wu
Wei
W
Applied Mathematics Department, Dalian University of Technology, Dalian 116023, China. wuweiw@dlut.edu.cn
Feng
Guorui
G
Li
Zhengxue
Z
Xu
Yuesheng
Y
D000465:Algorithms; D003198:Computer Simulation; D003199:Computer Systems; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted; D009862:Online Systems; D012815:Signal Processing, Computer-Assisted