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

Author

Zhang, Yong ; Du, Qing-Dong ; Yu, Shi-Dong ; Pan, Jeng-Shyang

Author_Institution

Software Coll., Shenyang Normal Univ., Shenyang, China

Volume

1

fYear

2009

fDate

12-14 Aug. 2009

Firstpage

467

Lastpage

470

Abstract

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.

Keywords

Kalman filters; fuzzy set theory; mining industry; radial basis function networks; safety; sensor fusion; RBF neural network; fusion learning; fuzzy evolution Kalman filtering; fuzzy information fusion; mine safety monitoring; nonlinear problem; Condition monitoring; Evolution (biology); Evolutionary computation; Filtering algorithms; Fuzzy neural networks; Kalman filters; Neural networks; Nonlinear filters; Probability; Safety; Kalman filtering; RBF neural network; information fusion; mine monitoring;

fLanguage

English

Publisher

ieee

Conference_Titel

Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on

Conference_Location

Shenyang

Print_ISBN

978-0-7695-3745-0

Type

conf

DOI

10.1109/HIS.2009.96

Filename

5254396