Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
407770 | Neurocomputing | 2012 | 10 Pages |
Abstract
In this paper, we study the convergence of an online gradient method with inner-product penalty and adaptive momentum for feedforward neural networks, assuming that the training samples are permuted stochastically in each cycle of iteration. Both two-layer and three-layer neural network models are considered, and two convergence theorems are established. Sufficient conditions are proposed to prove weak and strong convergence results. The algorithm is applied to the classical two-spiral problem and identification of Gabor function problem to support these theoretical findings.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Hongmei Shao, Dongpo Xu, Gaofeng Zheng, Lijun Liu,