基于遗传算法-BP神经网络的煤层注水效果分析

Effect analysis of coal seam water infusion based on genetic algorithm-BP neural network

  • 摘要: 为了提高BP神经网络预测煤层注水效果的精度,采用遗传算法优化BP神经网络的权值和阈值,建立了遗传算法-BP神经网络模型,并采用该模型对煤层注水湿润半径进行模拟预测。Matlab模拟结果表明,遗传算法-BP神经网络模型的预测结果比BP神经网络模型更准确,平均相对误差降低了40.29%,训练步数减少了1 665步,收敛速度快,稳定性好。

     

    Abstract: In order to improve prediction accuracy of coal seam water infusion effect by using BP neural network, genetic algorithm was used to optimize weight value and threshold value of BP neural network. Genetic algorithm-BP neural network model was built and used to predict wetting radius of coal seam water infusion. The Matlab simulation result shows that the genetic algorithm-BP neural network model has more accurate prediction result than BP neural network model, average relative error of the genetic algorithm-BP neural network model is reduced by 40.29%, training steps are reduced by 1 665 steps, convergence speed is fast and stability is good. Key words:

     

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