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

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.