独立成分分析在瓦斯浓度预测中的应用研究  

Application research of independent component analysis in gas concentration prediction

作　　者：龚星宇[1] 常心坦[2] 贾澎涛[1] 

机构地区：[1]西安科技大学计算机科学与技术学院,陕西西安710054 [2]西安科技大学能源学院,陕西西安710054

出　　处：《工矿自动化》2015年第4期82-86,共5页Journal Of Mine Automation

基　　金：陕西省自然科学基金资助项目(2012JQ8035);陕西省教育厅科研计划资助项目(2013JK0870)

摘　　要：为提高含噪声瓦斯浓度数据的预测精度,提出了一种基于独立成分分析(ICA)和k-最近邻(kNN)法的反向传播人工神经网络(BP-ANN)预测模型。利用滑动时间窗算法产生训练样本矩阵,采用ICA方法估计训练样本矩阵中的独立成分,用不含噪声的独立成分重新构建训练集;运用k-NN法减小训练集规模,引入混合距离测度函数降低训练过程的计算复杂度。实验结果表明,该预测模型较普通BP-ANN模型有效减小了瓦斯浓度预测误差和训练时间。In order to improve prediction accuracy of gas concentration with noise, a back-propagation artificial neural network（BP-ANN） prediction model based on independent component analysis（ICA） and k-nearest neighbor（k-NN） was proposed. Firstly, training samples are got by use of sliding time window algorithm, ICA is used to estimate independent component（IC） in the training samples, and training set is reconstructed with the IC which does not contain noise. Then, k-NN is used to reduce size of the training set and mixed distance measure function is introduced to reduce computation complexity of the training. The experimental results show that the prediction model effectively reduces prediction error and training time than traditional BP-ANN model.

关 键 词：瓦斯浓度预测 独立成分分析 k-最近邻法 反向传播人工神经网络 

分 类 号：TD712.5[矿业工程—矿井通风与安全]