title: Deterministic convergence of an online gradient method for BP neural networks.
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.
authors.affiliation: Applied Mathematics Department, Dalian University of Technology, Dalian 116023, China. wuweiw@dlut.edu.cn
authors.forename: Wei
authors.initials: W
authors.lastname: Wu
authors.forename: Guorui
authors.initials: G
authors.lastname: Feng
authors.forename: Zhengxue
authors.initials: Z
authors.lastname: Li
authors.forename: Yuesheng
authors.initials: Y
authors.lastname: Xu
mesh_terms: 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