Convergence of a Batch Gradient Algorithm with Adaptive Momentum for Neural Networks

In this paper, a batch gradient algorithm with adaptive momentum is considered and a convergence theorem is presented when it is used for two-layer feedforward neural networks training. Simple but necessary sufficient conditions are offered to guarantee both weak and strong convergence. Compared wit...

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Bibliographic Details
Published in:	Neural processing letters Vol. 34; no. 3; pp. 221 - 228
Main Authors:	Shao, Hongmei, Xu, Dongpo, Zheng, Gaofeng
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.12.2011 Springer Springer Nature B.V
Subjects:	Adaptive algorithms Applied sciences Artificial Intelligence Artificial neural networks Complex Systems Computational Intelligence Computer Science Computer science; control theory; systems Connectionism. Neural networks Convergence Error functions Exact sciences and technology Momentum Neural network Gradient algorithm Adaptive momentum Convergence Gradient Adaptive algorithm Error function Adaptive method Search algorithm Batch process Batch production Feedforward Quadratic function
ISSN:	1370-4621, 1573-773X
Online Access:	Get full text
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