ABSTRACT

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.

摘要

本文综合采用声发射监测信号，地应力监测信号，采矿生产数据，能量演化过程数据，微震统计，矿山地质结构和多学科数据等工程力学学科以及现有的认知规律来研究和建立径向基函数神经网络模型，用于时变过程信号分析。基于焦点区域定位和时空环境校正对齐，本研究以概率统计理论和大数据分析技术为基础。它研究了已经发生的微地震和周期性加权事件中的样本过程特征和控制规律，以研究统计规律，事件样本数据背后的趋势和关键特征，以便可以预测微震的幅度和风险程度。通过改变神经元的非线性变换函数的参数来实现连接权重调整的非线性映射和线性化，提高了网络的学习速度。与其他可以处理时变信号分类的动态神经网络模型相比，大大降低了计算复杂度。