摘要：This paper comprehensively adopts acoustic emission monitoring signals, ground stress monitoring signals, mining production data, energy evolution process data, microseismic statistics, mine geological structure and multidisciplinary data in subjects such as engineering mechanics, as well as existing cognitive laws to study and establish radial basis function neural network model for time-varying process signal analysis. Based on focal region localization and time-space environment correction alignment, the study bases itself on probability statistics theory and big data analysis technology. It studies sample process characteristics and the governing law in the already microseismic and periodic weighting events to investigate the statistical laws, trends and critical characteristics behind the event sample data so that microseismic magnitude and risk degree can be predicted. By changing the parameters of the nonlinear transformation function of the Neuron to realize the nonlinear mapping and the linearization of the connection weight adjustment, the learning speed of the network is improved. Compared with other dynamic neural network models which can deal with time-varying signal classification, the computational complexity is greatly reduced.
