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柴敬,王润沛,雷武林. 基于遗传-支持向量机的分布式光纤监测矿压时序预测[J]. 科学技术与工程, 2020, 20(32): 13137-13142.
ChaiJin,wang runpei,lei wulin.Time Series Prediction of Distributed Optical Fiber Monitoring Rock Pressure Based on GA-SVR[J].Science Technology and Engineering,2020,20(32):13137-13142.
基于遗传-支持向量机的分布式光纤监测矿压时序预测
Time Series Prediction of Distributed Optical Fiber Monitoring Rock Pressure Based on GA-SVR
投稿时间:2019-10-13  修订日期:2020-08-06
DOI:
中文关键词:  相空间 分布式光纤 遗传算法 支持向量机 神经网络
英文关键词:phase space distributed Optical Fiber genetic algorithm Support Vector Machine neural network
基金项目:国家自然学科基金资助项目(41027002,51804244)
        
作者单位
柴敬 西安科技大学能源学院
王润沛 西安科技大学
雷武林 西安科技大学
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中文摘要:
      为了有效的掌握岩层内部变形,准确预测开采过程中的矿压显现规律。采用分布式光纤监测覆岩内部变形并结合支持向量机计算方法,将光纤频移变化度作为主要特征参数,构建混沌矿压数据相空间,采用遗传算法(GA)对支持向量机回归(SVR)超参数寻优。开展相似材料模型试验,模拟工作面开采,并引入光纤频移变化度概念,建立GA-SVR时序预测模型。预测结果与传统回归模型(SVR)和BP神经网络模型(BPNN)进行比较。结果表明,BPNN容易发生过拟合,传统SVR模型依赖超参数选取,GA-SVR模型在超参数选取上更科学,不容易发生过拟合,预测精度高于上述两种算法,为矿压时序预测定量化提供科学依据。
英文摘要:
      In order to effectively grasp the mechanism of internal deformation of rock strata, Accurate prediction of rock pressure law in excavation process。Distributed optical fiber is used to monitor the internal deformation of rock mass and support vector machine is used,Optical fiber frequency shift variation as the main characteristic parameter,The phase space of chaotic rock pressure data is constructed,Superparametric optimization of support vector machine regression (SVR) using genetic algorithm (GA)。Developing Model Tests of Similar Materials,Mining in simulated working face ,The concept of frequency shift variation of optical fibers is introduced ,Establishment of GA-SVR Time Series Prediction Model 。The prediction results are compared with traditional regression model (SVR) and BP neural network model (BPNN)。The results show that BPNN is prone to over-fitting, and the traditional SVR model depends on the selection of super-parameters,GA-SVR model is more scientific in super-parameter selection ,Not easy to,The prediction accuracy is higher than the two algorithms mentioned above。fit It has good generalization and provides scientific basis for the quantitative prediction of rock pressure time series。
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