The Neural Network Based On Rough Set Theory

摘要：

In order to simplify the complexity of the BP network structure and reduce the time of training samples. The article simplifies the complexity of BP network structure through the research of the BP network and rough set, removes the samples redundant attribute using the rough set attribute reduction theory, and trades the reduction after the BP network data as training samples; It also reduces the time of training samples by training the samples with the Conjugate Gradient Algorithm with Momentum and Batch Techniques. The experimental results show the effectiveness of the method.

关键词： Rough set BP network reduction training samples

作者: Kuan-Cheng Zou Li-Li Bing Yan-Bin Yang

作者单位: College of Computer Science and Engineering Changchun University of Technology Changchun, China

会议类型: 国际会议

会议名称: 2010 International Conference on Computer,Mechatronics,Control and Electronic Engineering(2010计算机、机电、控制与电子工程国际会议 CMCE 2010)

会议地点: 长春

会议语种:英文

页码: 282-285

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）