辽宁工程技术大学学报（自然科学版）2017，Vol.36Issue(4)：359-365,7.DOI:10.11956/j.issn.1008-0562.2017.04.005
煤矿瓦斯涌出量的非线性降维Elman动态预测模型
Nonlinear dimension reduction and improved Elman dynamic prediction model of coal mine gas emission
摘要
Abstract
In order to achieve more effective predicted results for the absolute gas emission quantity,this paper put forward gas emission dynamic prediction model based on the nonlinear dimension reduction and the improved Elman.This model uses the nonlinear mapping in the feature space to reduce data dimension effectively and to determine the input numbers of neural network.For the purpose of achieving the optimal parameters of the improved Elman neural network(IENN),this paper used the adaptive ant colony-differential evolution algorithrn(ACDE).With the historical data of mine actual monitoring to experiment and analysis,the results show that this model can effectively reduce the numbers of input variables,and compared with other prediction models this model improves the forecast accuracy and efficiency.关键词
绝对瓦斯涌出量/非线性映射/蚁群算法/微分进化算法/Elman神经网络Key words
absolute gas emission quantity/nonlinear mapping/ant colony algorithm/differential evolution algorithm/Elman neural network分类
矿业与冶金引用本文复制引用
魏林,付华,尹玉萍..煤矿瓦斯涌出量的非线性降维Elman动态预测模型[J].辽宁工程技术大学学报（自然科学版）,2017,36(4):359-365,7.基金项目
国家自然科学基金项目(51274118) （51274118）
