ACPSO-BP神经网络在矿井突水水源判别中的应用

矿井多年来的连续开采使各含水层水质特征变得更加复杂、更为接近,应用经典数学方法难以建立精确的判别模型,使用具有非线性映射功能的BP神经网络可以克服以上问题,但其仍然具有易陷入局部最优和收敛速度慢缺点。通过将"早熟"判断机制、Tent混沌映射以及权重自适应调整策略引入粒子群算法中,建立基于自适应混沌粒子群算法和BP(ACPSO-BP)神经网络突水水源判别模型,应用结果表明:与BP神经网络模型、基于标准粒子群算法和BP(SPSO-BP)神经网络模型相比,ACPSO-BP神经网络模型具有收敛速度快、精度高和泛化能力强的特点。

Application of ACPSO-BP neural network in discriminating mine water inrush source

The continuous mining in the coal mine has made the water quality characteristics of each aquifer become more complex and more similar, and it is difficult to establish a precise discriminating model by using classical mathematical methods. The BP neural network with nonlinear mapping function can overcome the above problems, but it still has the disadvantage of being easy to fall into local optimization and having slow convergence speed. We introduced the "premature" judgment mechanism, Tent chaos map and adaptive weight adjusting strategy into particle swarm optimization algorithm, establishing a water inrush discrimination model on the basis of adaptive chaos particle swarm optimization algorithm and BP(ACPSO-BP) neural network. The application results show that compared with the BP neural network model and the model based on standard particle swarm algorithm and BP(SPSO-BP) neural network, the ACPSO-BP neural network model has faster convergence speed, higher accuracy and stronger generalization.