Forecasting of underground field intensity based on LS-SVM optimized by genetic algorithm
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摘要: 为了进一步研究井下电波传播损耗规律，提高场强覆盖预测准确度，提出使用基于遗传算法优化的最小二乘支持向量机方法对井下巷道的场强进行预测。首先通过软件仿真生成巷道场强数据，并将数据分为训练集和测试集；然后采用最小二乘支持向量机方法对训练集进行学习，并使用遗传算法对最小二乘支持向量机方法的参数选择进行优化，采用测试集对方法性能进行验证；最后将基于遗传算法优化的最小二乘支持向量机方法用于井下巷道的场强预测。仿真实验结果表明，基于遗传算法优化的最小二乘支持向量机方法能够有效提高井下场强预测的精度，可获得较好的预测效果。Abstract: In order to study propagation loss law of electric wave and improve prediction accuracy of field intensity coverage in mine tunnel, least square support vector machine (LS-SVM) method optimized by genetic algorithm was used to forecast underground field intensity in mine tunnel. Firstly, simulated field intensity data was generated by computer software and divided into training set and testing set. Then the LS-SVM machine method was used to study training set, genetic algorithm was used to optimize parameters of LS-SVM, and testing set was used to verify performance of the method. Finally the LS-SVM method optimized by genetic algorithm was used to forecast underground field intensity in mine tunnel. The simulation and experiment results prove that the LS-SVM optimized by genetic algorithm can effectively improve prediction accuracy of field intensity in mine tunnel, and achieve good prediction effect.
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期刊类型引用(1)
1. 单杰，关丙火. 井下履带式探测机器人及其运动抗扰控制研究. 工矿自动化. 2022(02): 100-106+146 . 
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