摘要
Many monitoring measures were used in the production field for predicting rockburst.However, predicting rock burst according to complicated observation data is alwaysa pressing problem in this research field.Though the critical value method gets extensiveapplication in practice, it stresses only on the superficial change of data and overlooks alot of features of rock burst and useful information that is concealed and hidden in the observationtime series.Pattern recognition extracts the feature value of time domain, frequencydomain and wavelet domain in observation time series to form Multi-Feature vectors,using Euclidean distance measure as the separable criterion between the same typeand different type to compress and transform feature vectors.It applies neural network asa tool to recognize the danger of rock burst, and uses feature vectors being compressedto carry out training and studying.It is proved by test samples that predicting precisionshould be prior to such traditional predicting methods as pattern recognition and critical indicatormethod.
Many monitoring measures were used in the production field for predicting rock burst. However, predicting rock burst according to complicated observation data is always a pressing problem in this research field. Though the critical value method gets extensive application in practice, it stresses only on the superficial change of data and overlooks a lot of features of rock burst and useful information that is concealed and hidden in the observation time series. Pattern recognition extracts the feature value of time domain, frequency domain and wavelet domain in observation time series to form Multi-Feature vectors, using Euclidean distance measure as the separable criterion between the same type and different type to compress and transform feature vectors. It applies neural network as a tool to recognize the danger of rock burst, and uses feature vectors being compressed to carry out training and studying. It is proved by test samples that predicting precision should be prior to such traditional predicting methods as pattern recognition and critical indicator method.
