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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Neural processing letters Ročník 34; číslo 3; s. 221 - 228
Hlavní autoři:	Shao, Hongmei, Xu, Dongpo, Zheng, Gaofeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Boston Springer US 01.12.2011 Springer Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!