摘要
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.
作者
徐星
李喆
XU Xing1, LI Zhe2(1. School of Safety Engineering, Henan Institute of Engineering, Zhengzhou 451191, China; 2. Vocational Education Center of Yi Coal Corporation, Yima 472300, Chin)
出处
《煤炭技术》
CAS
2018年第6期157-158,共2页
Coal Technology
基金
国家自然科学基金资助项目(51604091
51474094)
河南省科技攻关计划项目(172102310738
182102310743)
河南省高等学校重点科研项目(18A440010)
