A Geographic Information Knowledge Discovery Model Based on Rough Set and Neural Network

摘要：

The paper proposes a model based on rough set theory and neural network technology to discover knowledge from geographic information that has high spatial autocorrelation and fuzzy characteristics. In the model first get the most concise if-then rules by discernibility matrix. Then construct a three-layer neural network to simulate the most concise rules. Inputs and outputs of the neural network are determined by the parameter-training method that is provided in this paper. Finally the paper presents a simulation of its use for judging drought and flood disasters in Songliao River base. The results show that the model can quickly form the most concise rules and make right decision.

关键词： rough set theory neural network rules

作者: Sun Yannan Li Xiumei

作者单位: School of Electronic Information Engineering Dalian Jiaotong University Dalian,China Software Technology Institute Dalian Jiaotong University Dalian,China

会议类型: 国际会议

会议名称: 2009 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2009)(2009年检测技术与机械自动化国际会议)

会议地点: 长沙

会议语种:英文

页码: 1394-1397

在线出版日期: 2009-04-11（万方平台首次上网日期，不代表论文的发表时间）