Rockburst Prediction Method Based on Nonlinear Fractional-order Median Discriminative Space Learning
FAN Tengyue
SU Shuzhi
ZHU Yanmin
Abstract：To address the issues of low accuracy of rockburst grade prediction due to high noise and small number of rockburst sample data,a prediction method based on nonlinear fractional-order median discriminative space learning(NFMDSL)was proposed.The class sample medians were used to replace class sample means,resulting in the construction of a median discriminative space learning method that better preserved effective sample information and reduced the impact of noise on prediction performance.To effectively capture the nonlinear discriminative structures among rockburst data,the sample data were further projected into a kernel space using kernel techniques.Additionally,fractional-order methods were introduced to re-estimate the eigenvalues and singular values of the scatter matrix,enabling the extraction of rock burst features with strong discriminative capabilities from a limited number of samples.The results indicated that the NFMDSL method achieved an average accuracy of 95.75％in rock burst level prediction and it demonstrated higher accruacy and stronger robustness compared to other methods.This method can be applicable to rockburst prediction in the fields of mining and tunnel engineering.
Keywords：rockburst predictionclass mediankernel techniquesscatter matrixsingular valuesmall sample sizemining and tunnel engineering
Publication Date：2024-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 480-485,513 )