Open AccessConvergence Analysis of Multilayer BP Neural Network with Momentum Term
Author(s) -
Xiang Xu,
Gang Xie
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1650/3/032123
Subject(s) - convergence (economics) , momentum (technical analysis) , term (time) , artificial neural network , constant (computer programming) , rate of convergence , mathematical proof , mathematics , algorithm , computer science , normal convergence , artificial intelligence , physics , key (lock) , geometry , financial economics , economics , economic growth , computer security , quantum mechanics , programming language
In this paper, we analyze the convergence of a back-propagation (BP) neural network with momentum term containing multiple hidden layers. When the learning rate is constant and the momentum coefficient is adaptively changed under certain conditions, we give both the weak and strong convergence results of the algorithm, and give corresponding theoretical proofs for both convergence results.