基于参数自适应差分进化算法的煤矿开采沉陷预计

    Coal Mine Mining Subsidence Prediction Based on Parameter Adaptive Differential Evolution Algorithm

    • 摘要: 通过模拟实验研究基于参数自适应差分进化算法的数值模型参数反演的可行性,在此基础上结合工程实例开展开采沉陷预计,并利用差分技术对数值模型进行误差控制。研究结果表明:采用参数自适应差分进化算法进行参数反演收敛速度快,能提高参数反演效率,并可在短期内,使用差分技术对数值模型的误差进行控制,提高煤矿开采沉陷预计的精度。

       

      Abstract: We study the feasibility of parameter inversion experiment of numerical model based on parameter adaptive differential evolution algorithm by simulation experiment. On this basis, we carry out mining subsidence prediction combined with engineering examples. Results show that using the parameter adaptive differential evolution algorithm to carry out parameter inversion can improve the efficiency and convergence rate of parameter inversion; in the short term, using differential techniques to control error of numerical model can improve coal mining subsidence prediction accuracy.

       

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