煤矿安全2017，Vol.48Issue(12)：150-152,156,4.DOI:10.13347/j.cnki.mkaq.2017.12.040
基于LVQ-GA-BP神经网络的煤矿瓦斯涌出量预测
Prediction for Coal Mine Gas Emission Based on LVQ-GA-BP Neural Network
摘要
Abstract
We combine the learning vector quantization (LVQ) and GA-BP neural network to predict gas emission in the view of the characteristics of varied,nonlinear and complex gas emission in coal mine.The algorithm classified and selected the main influence factors,and used genetic algorithm to optimize the weight and threshold of BP neural network to construct the prediction model of mine gas emission quantity.LVQ-GA-BP prediction model established by related data is compared and analyzed with BP neural network,the result shows that the average relative error of this method is 0.025 51 and is lower than that of the BP neural network,and the method improves the prediction accuracy.关键词
LVQ神经网络/遗传算法/BP神经网络/煤矿瓦斯涌出量/预测Key words
LVQ neural network/genetic algorithm/BP neural network/gas emission of coal mine/prediction分类
矿业与冶金引用本文复制引用
谢丽蓉,王晋瑞,穆塔里夫·阿赫迈德,路朋,牛永朝..基于LVQ-GA-BP神经网络的煤矿瓦斯涌出量预测[J].煤矿安全,2017,48(12):150-152,156,4.基金项目
国家自然科学基金资助项目(51264036) （51264036）
