Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Online learning from finite training sets and robustness to input bias
Neural Computation
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Deterministic convergence of an online gradient method for neural networks
Journal of Computational and Applied Mathematics - Selected papers of the international symposium on applied mathematics, August 2000, Dalian, China
Convergence of an online gradient method for feedforward neural networks with stochastic inputs
Journal of Computational and Applied Mathematics - Special issue on proceedings of the international symposium on computational mathematics and applications
Stability of steepest descent with momentum for quadratic functions
IEEE Transactions on Neural Networks
Convergence of gradient method with momentum for two-Layer feedforward neural networks
IEEE Transactions on Neural Networks
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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An online gradient method with momentum for feedforward neural network is considered. The learning rate is set to be a constant and the momentum coefficient an adaptive variable. Both the weak and strong convergence results are proved, as well as the convergence rates for the error function and for the weight.