用于神经网络计算的带惩罚项的线梯度法

Online gradient methods are widely used for training the weight of neural networks and for other engineering computations,In certain cases,the resulting weight may become very large,causing difficulties in the implementation of the network by electronic circuits.In this paper we introduce a punishing term into the error function of the training procedure to prevent this situation.The corresponding convergence of the iterative training procedure and the boundedness of the weitht sepuence are proved.A supporting numerical example is also provided.     

Online Gradient Methods with a Punishing Term for Neural Networks

1 IntroductionOnline gradient methods (OGM, for short) are widely used for training neuraJ networks (cf.Il,2,3,4]). Its iterative convergence for linear models is proved in e.g. I5,6,71. A nonlinearn1odel is considered in [8]. During the iterative training procedure, sometimes (see the nextsection of this paper)the weight of the network may become very laxge, causing difficultiesin the implementation of the network by electronic circuits. A revised error fUnction ispresented in [gl to prev…     

上海股票市场收益的随机波浪模型

对金融资产收益分布状况的主要研究方法是先提出分布模型,然后进行实验验证;因缺乏必要的机理分析和研究手段单一,使其理论研究和应用研究都受到一定的制约.为克服这些不足,将金融资产收益联系起来看,根据其涨跌周期性构建出随机波浪模型,并利用模型导出随机波浪波高和周期的分布公式.通过实证分析,证明随机波浪模型具有一定的适用性;所用的时频分析方法以及所得结论有益于对金融资产收益分布状况进行更深入的理论和应用研究,也有益于指导市场参与者进行短期和长期交易.