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《Electronics World》 2014-11
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Rockburst Prediction Algorithm on multi-source chaos time-series using SVM

Yang Chun-dong;Kunming Design and Research Institute of Coal;  
Allowing for the limitations of single variable to describe system,rockburst risk is predicted on multi-source monitoring chaos time-series to improve the prediction precision.Because the time-series to monitor rockburst have chaotic characteristics and their reconstruction state vectors can better reflect the structure change of coal and rock in mining process,the paper uses the state vectors as input variables of prediction model and gives the principle to identify rockburst risk.Considering the redundancy and complementarity among the features of the state vector,the random subspace ensemble learning algorithm on feature clustering is presented,and the algorithm's details including feature clustering,feature subspace structuring and individual learners training are explained.Finally,the proposed method is applied to 25110 mining face and the results show that our method can significantly improve prediction precision,relative to the method of taking multi-source monitoring variables directly as prediction model inputs.
【CateGory Index】: TD324
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