Rock burst prediction on multivariate chaotic time series
Abstract：Given rock burst chaotic characteristics and its limited-length monitor data containing noise,the multiple rock burst monitor variants were predicted on multivariate time series reconstruction and generalized regression neural network(GRNN).The theories of multivariate phase space reconstruction and GRNN prediction were introduced,and the method was proposed that adopting genetic algorithm to simultaneously determine reconstruction parameters and GRNN smoothing parameter,to ensure prediction precision.In Matlab2010a environments,the method was simulated on Lorenz system to verify its effectiveness for limited-length multivariate series containing noise.Finally the method was used to microseism energy and electromagnetic radiation signal monitor data,and the results show that the prediction method on multivariate chaotic series can predict multiple monitor variants and therefore forecast rock burst even in the case of relatively limited-history data.
Keywords：rock burstchaos predictionmultivariate time seriesphase space reconstructionGRNNgenetic algorithm
Publication Date：2012-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 1624-1629 )