An online gradient method with momentum for two-layer feedforward neural networks

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 stabilize the learning procedure of the network weights. Corresponding convergence results are proved, that is, the weak convergence result is proved under the uniformly boundedness assumption of the activation function and its derivatives, moreover, if the number of elements of the stationary point set for the error function is finite, then strong convergence result holds.