CAJ | 학술논문

　　为有效确定概率积分法预计参数，提高预计值的精度。将粒子群优化(PSO)算法和BP神经网络进行融合，采用改进的混合粒子群优化算法优化神经网络的权值和阈值。在分析概率积分法参数与地质采矿条件之间关系的基础上，建立了基于PSO优化BP神经网络的概率积分法预计参数的优化选择模型。以我国典型的地表移动观测站资料作为网络的学习训练样本和测试样本，将计算结果与实际值进行了对比分析，并与改进BP算法的计算结果进行了比较。结果表明，PSO-BP神经网络方法用于概率积分法预计参数的选取是可行的，收敛速度更快，计算精度更高。
　　위유효학정개솔적분법예계삼수，제고예계치적정도。장입자군우화(PSO)산법화BP신경망락진행융합，채용개진적혼합입자군우화산법우화신경망락적권치화역치。재분석개솔적분법삼수여지질채광조건지간관계적기출상，건립료기우PSO우화BP신경망락적개솔적분법예계삼수적우화선택모형。이아국전형적지표이동관측참자료작위망락적학습훈련양본화측시양본，장계산결과여실제치진행료대비분석，병여개진BP산법적계산결과진행료비교。결과표명，PSO-BP신경망락방법용우개솔적분법예계삼수적선취시가행적，수렴속도경쾌，계산정도경고。
In order to effectively determine the prediction parameters of probability-integral method and to improve the prediction accuracy, a new method by combining Particle Swarm Optimization (PSO) algorithm and BP neural network (PSO-BP) was presented. In this method, an improved hybrid PSO algorithm was used to optimize the connection weights and thresholds values of BP neural network. An optimization model for prediction parameters of probability-integral method using this hybrid PSO-BP neural network algorithm was constructed based on analyzing the relationship between the parameters and geological mining conditions. Typical data of surface moving observation stations was used as learning and test samples. Analysis was made by comparing calculated values, observed values, and values of improved BP neural network. Results indicate that PSO-BP calculation model has higher con-vergence speed and higher precision.