【摘要】 覆盖算法是神经网络学习算法中的一个十分有效的方法 ,它克服了基于搜索机制的学习方法和规划学习方法计算复杂性高 ,难以用于处理海量数据的不足 ,为神经网络提供一个构造性的学习方法 但该方法是建立在所有训练样本都是精确的假设上的 ,未考虑到所讨论的数据具有不精确的情况 ,若直接将该方法应用于数据不精确情况 ,所得到效果不理想 主要讨论数据具有不精确情况下的时间序列的预测问题 为此将原有的覆盖算法进行改进 ,引入“覆盖强度”和“拒识样本”的概念 ,并结合这些新概念给出相应的覆盖学习算法 (简称SLA) ,最后将SLA算法 ,应用于金融股市的预测 ,具体应用到以上 (海 )证 (券 )综合指数构成的时间序列的预测 ,取得了较好的结果 ,这表明了SLA方法的可行性和应用前景更多还原

【Abstract】 Covering algorithms are very useful learning methods of neural networks, and their computing complexity is lower than that of the learning method based on search mechanism or programming based learning algorithm Covering algorithms not only are applied to deal with vast data set but also provide a new constructive learning method of neural networks However, they are based on the assumption that all of the training samples are accurate and the instance that some of the training samples are not accurate is not discussed If the methods are applied to no accurate data directly, the result is not satisfactory Discussed in this paper is the problem of forecasting time series where there are some no accurate data The covering algorithm improved and the definitions of covering intensity and no acknowledge sample are introduced The improved covering algorithm is called a structural learning algorithm (SLA) SLA is applied to forecasting a time series which is composed of Shanghai’s stock integrating index, and the satisfying results are achieved It is expected that SLA will have wide applications更多还原