欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
针对单个神经网络模型易出现过拟合而导致泛化能力较弱的缺点,引入了神经网络集成方法,对传统的Bagging方法进行改进,提出了一种基于0.632误差聚类的Bagging方法。通过实验对比和假设检验,证实了该方法的优越性,并探讨了最佳聚类数目。最后,通过应用实例展示了利用集成神经网络进行产品完工期预测的全过程。实验结果显示,该方法明显地提高了预测精度。
英文摘要：
To overcome the poor generalization ability of a single neural network resulting from the over fitting,neural network ensemble was introduced,and a new Bagging approach based on the cluster analysis of the 0.632 prediction error was proposed.Advantages of this proposed method was validated by experimental comparison and hypothesis test.The best number of clusters was also discussed.Finally,a case study was given to illustrate the whole steps to predict the product due date by using neural network ensemble.Results showed that this model could remarkably improve precision of prediction results.
同期刊论文项目
同项目期刊论文
Integration of rough set and neural network ensemble to predict the configuration performance of mod
期刊信息
位置：