| CONVERGENCE OF ONLINE GRADIENT METHOD WITH A PENALTY TERM FOR FEEDFORWARD NEURAL NETWORKS WITH STOCHASTIC INPUTS |
| |
| 作者姓名： | 邵红梅 吴微 李峰 |
| |
| 作者单位： | [1]DepartmentofAppliedMathematics,DalianUniversityofTechnology,Dalian116024,PRC. [2]DepartmentofAppliedMathematics,DalianUniversityofTechnology,Dalian116024,PRC. |
| |
| 基金项目： | Partly supported by the National Natural Science Foundation of China,and the Basic Research Program of the Committee of Science，Technology and Industry of National Defense of China. |
| |
| 摘 要： | Online gradient algorithm has been widely used as a learning algorithm for feedforward neural network training. In this paper, we prove a weak convergence theorem of an online gradient algorithm with a penalty term, assuming that the training examples are input in a stochastic way. The monotonicity of the error function in the iteration and the boundedness of the weight are both guaranteed. We also present a numerical experiment to support our results.
|
| 关 键 词： | 前馈神经网络系统 收敛 随机变量 单调性 有界性原理 在线梯度计算法 |
| 本文献已被 CNKI 维普 等数据库收录！ |
|
