基于GA-BP网络模型的煤矿底板突水非线性预测评价

以非线性预测评价为基础,采用BP神经网络模型,利用遗传算法优化网络初始权值和阈值,建立一个新的煤矿底板突水危险性预测的网络模型,通过收集不同突水矿井的资料,综合考虑多种影响底板突水的因素。运用Matlab编程对网络原始数据进行训练,并对不同工作面底板是否突水及突水量进行预测分析,结果表明,该模型收敛速度快、预测精确度高,且具有较强的泛化能力。

Nonlinear Prediction and Evaluation of Coal Mine Floor Water Inrush Based on GA-BP Neural Network Model

Based on nonlinear prediction and evaluation,and using the BP neural network model,using genetic algorithms to optimize the network initial weights and thresholds of the network model,a new coal mine inrush water risk prediction is built by collecting different mine water inrush datum and considering a variety of water inrush factors.Using matlab programming to train the network initial datum,and to ananlyze whether the different working face floor inrush water or not and the quantity of water inrush,the results show that the model is fast convergence strong generalization ability,forecast precision.