粗糙集BP神经网络在高校贫困生认定中的应用  

Application on Identification of Poor Students Based on Rough Set and BP Neural Network

作　　者：邵为爽[1] 刘树东[1] 

机构地区：[1]齐齐哈尔大学,黑龙江齐齐哈尔161006

出　　处：《煤炭技术》2012年第6期169-171,共3页Coal Technology

基　　金：黑龙江省自然科学基金(A201014)

摘　　要：研究高校贫困生的认定问题,构建了基于粗糙集的BP神经网络模型。该模型首先应用基于Pawlak属性重要度的属性约简算法得到认定学生贫困的重要属性,降低决策表的维数,然后将降维后的数据送入BP神经网络进行学习和训练,最后采用训练好的的网络对测试样本进行检验。To research the identification of poor students, this paper constructs a BP neural network model based on rough set. Attribute reduction algorithm based on Pawlak attribute significance is firstly used to obtain the mainly components of the identification of poor students to reduce the number of dimensionalities of the decision talbe. After the dimensionality reduction process, we put the new data into BP neural network to train it.

关 键 词：粗糙集 神经网络 属性约简 贫困生认定 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]