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基于多变量混沌时间序列的煤矿斜井TBM施工动态风险预测
Risk analysis on long inclined-shaft construction in coalmine by TBM techniques based on multiple variables chaotic time series
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摘要: 在全面分析煤矿长斜井TBM(盾构)施工动态风险特点的基础上，利用多变量混沌时间序列预测方法对其进行预测。利用主成分分析法，确定影响煤矿长斜井TBM施工风险的主要成分。对煤矿长斜井TBM施工风险多变量时间序列进行相空间的重构，确定时间延迟τi和嵌入维数mi ，采用小数据量法计算煤矿长斜井TBM施工多变量风险时间序列的最大Lyapunov指数，证明了其具有混沌特性，提出了一阶局域法与双隐层神经网络的组合预测模型，该模型能够对多变量风险时间序列随时间的变化进行预测。仿真实验表明，该预测模型误差小于单变量时间序列的预测误差，具有较强的预测能力和较好的预测效果，可为煤矿长斜井TBM施工风险分析与评估提供一种新的途径。Abstract: Multi-variable chaotic time series are used to predict the long inclined-shaft construction in coalmine construction by TBM techniques, and principal component analysis (PCA) is used to determine the main factors that impact risk (shield) of the long inclined-shaft construction in coalmine by TBM techniques. Phase space of risk time series for construction by TBM are reconstructed; time delay and embedding dimension are determined. Maximum Lyapunov indexes of risk are obtained by using small data quantity method;it is found that the time series have characteristics of chaos. Prediction model is established using the combination of first-order local method and double hidden layer neural network. Simulation experiments show that the combined model has a strong ability of prediction and achieves better effect. As a result, it provides a new way for long inclined-shaft construction in coalmine by TBM techniques.
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