@Article{CMR-26-1,
author = {Shao, Hongmei and Wu, Wei and Liu, Lijun},
title = {Convergence of Online Gradient Method with Penalty for BP Neural Networks},
journal = {Communications in Mathematical Research },
year = {2010},
volume = {26},
number = {1},
pages = {67--75},
abstract = {<p style="text-align: justify;">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 order within each epoch. The monotonicity of the error
function with penalty is also guaranteed in the training iteration. Simulation results
for a 3-bits parity problem are presented to support our theoretical results.</p>},
issn = {2707-8523},
doi = {https://doi.org/2010-CMR-19174},
url = {https://global-sci.com/article/81891/convergence-of-online-gradient-method-with-penalty-for-bp-neural-networks}
}