RBF Neural Network based on Fuzzy Evolution Kalman Filtering and Application in Mine Safety Monitoring

摘要：

Fuzzy information fusion methods are adopted widely to resolve the complicated nonlinear problems in recent years. This paper proposes a fusion learning algorithm of radial basis function (RBF) neural network based on fuzzy evolution Kalman filtering. By using this proposed method, monitoring data are extracted and optimized in mine safety monitoring, and Matlab simulation results are analyzed. The results show that this method has feasibility and rapid learning efficiency, which can improve precision and reliability in mine monitoring systems.

关键词： information fusion RBF neural network Kalman filtering mine monitoring

作者: Yong Zhang Qing-dong Du Shi-dong Yu Jeng-Shyang Pan

作者单位: software college Shenyang Normal University Shenyang, China Electronic Eng. National Kaohsiung University of Applied Sciences Kaohsiung, Taiwan

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-4

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）