CONVERGENCE OF ONLINE GRADIENT METHOD WITH A PENALTY TERM FOR FEEDFORWARD NEURAL NETWORKS WITH STOCHASTIC INPUTS

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.     

高效液相色谱法测定盐地碱蓬汁中7种水溶性维生素和18种氨基酸

 采用Ｓｈｉｍ－ｐａｃｋＣＬＣ－ＯＤＳＣ_（１８）色谱柱和ＡＡ０３反相键合相柱，对盐地碱蓬（以下称碱蓬）汁中７种水溶性维生素和１８种氨基酸进行了分离测定，结果令人满意。     

带动量项的线性输出BP网络收敛性

1 前言线性输出的BP神经网络已被成功应用于解决模式分类和函数逼近等问题[1-2],有关 BP学习算法的收敛性研究可参见文献[3-5]．但是,传统BP算法具有两大缺点:收敛速度慢和容易陷入局部极小．一种常见的改进策略是在它的权值更新规则中加入动量项,即上一步权值改变量的某一倍数,使得当前步的权值改变不仅受到梯度下降方向的影响,