【摘要】 如何从电磁辐射监测数据中预测冲击地压危险性,一直是冲击地压预测领域研究的重点。目前,运用较多的有临界指标法、综合指数法、指标变化偏差法等,但这些方法都只注重数据的表面变化,忽视了隐藏及蕴含在监测时序中许多有助于识别冲击地压危险的特征和有用信息,模式识别方法通过提取监测时序中的时域、频域及小波域特征构成复合特征向量,以欧氏距离测度作为类内类间可分离性判据对特征向量进行压缩变换,运用Fisher准则构造冲击危险性识别的模式识别器,识别器用压缩后的特征向量进行训练学习,得到识别器的结构参数:权向量和分界值(阈值)后,即可成为性能稳定的冲击地压危险性预测的模式识别系统,运用该系统就能实现对其他样本的预测识别。通过对检验样本的预测表明,预测精度要优于临界指标法等传统预测方法。更多还原

【Abstract】 How to predict rock burst according to electromagnetic emission observation data has been always the hot topic in this research field.The methods more often used now are critical value method,synthetically index method and index change error method,etc..But all these methods lie stress only on the superficial change of data and overlook a lot of features of rock burst and useful information which are concealed and hidden in observation time series.Pattern recognition extracts the feature value of time domainf,requency domain and wavelet domain in observation time series to form multi-feature vectors,use Euclidean distance measure as separable criterion between the same type and different types to compress and transform feature vectors,applies Fisher criterion to form pattern recognizer for dangeorous recognition.The pattern recognizer uses feature vectors being compressed to carry out training and study and gets the structure parameter:discriminate coefficient(weight value) and discriminate threshold.It has become the pattern recognition system with stable function.It is proved by prediction of test sample that predicting precision is prior to traditional predicting methods such as critical value method and so on.更多还原