基于改进粒子群BP神经网络的矿井突水水源判别

Identification of Mine Water Inrush Source Based on Improved Particle Swarm Optimization and BP Neural Network

选取K^++Na^+、Ca^(2+)、Mg^(2+)、Cl^-、HCO_3^-、SO_4^(2-)6种离子作为判别指标,提出基于Tent混沌映射的自适应混沌粒子群算法(ACPSO),使自适应混沌粒子群算法快速、高效地对BP神经网络完成最优初始化,并将建立的ACPSO-BP神经网络突水水源判别模型进行实例应用。

K ＋＋Na ＋, Ca2＋ . Mg2＋ , Cl-, HCO3-, SO4^2- 6 ions were selected as the basis of identification, an adaptive chaos particle swarm optimization algorithm based on Tent chaotic mapping （ACPSO） were introduced, the adaptive chaos particle swarm optimization algorithm was used to quickly and efficiently perform the optimal initialization of the BP neural network,then ACPSO-BP neural network model of water inrush identification was established and applied.