In this paper, we propose a novel machine learning classifier by deriving a new adaptive
momentum back-propagation (BP) artificial neural networks algorithm. The proposed …

This paper considers a class of online gradient learning methods for backpropagation (BP)
neural networks with a single hidden layer. We assume that in each training cycle, each …

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 …

The online backpropagation (BP) training procedure has been extensively explored in
scientific research and engineering applications. One of the main factors affecting the …

This work proves the deterministic convergence of the Sigma-Pi-Sigma neural network
based on the batch gradient learning algorithm under certain relaxed conditions. We …

Minimization of the training regularization term has been recognized as an important
objective for sparse modeling and generalization in feedforward neural networks. Most of …

A gradient method with momentum for two-layer feedforward neural networks is considered.
The learning rate is set to be a constant and the momentum factor an adaptive variable. Both …

In this brief, we consider an online gradient method with penalty for training feedforward
neural networks. Specifically, the penalty is a term proportional to the norm of the weights. Its …

An online gradient method with momentum for two-layer feedforward neural networks is
considered. The momentum coefficient is chosen in an adaptive manner to accelerate and …

Two backpropagation algorithms with momentum for feedforward neural networks with a
single hidden layer are considered. It is assumed that the training samples are supplied to …