Time-Series-Short-Term

代码说明：

  针对神经网络的瓦斯预测模型存在的泛化性能差且存在易陷入局部最优的缺点，提出了 基于最小二乘支持向量机(LS-SVM)时间序列瓦斯预测方法．由于标准最小二乘支持向量机 (L孓SVM)要求样本误差分布服从高斯分布，且标准LS-SVM丧失鲁棒性与稀疏性等特点，提出 了基于加权LS-SVM的瓦斯时间序列预测的方法，从而提高了标准L孓SVM模型的鲁棒性．其 中时间序列的嵌入维数与延迟时间采用了微熵率最小原则进行选取，在此基础上给出了基于加 权L孓SVM实现多步时间序列预测的算法实现步骤．最后利用MATLAB 7．1对其进行仿真研 究，通过鹤壁十矿1个突出工作面的瓦斯涌出数据实例对模型进行了验证．结果表明，加权 SVM模型比标准的L§SVM明显提高了鲁棒性，可较好地实现时间序列数据的多步预测．(The neural network gas prediction model is poor in generalization performance and easy in fafling into the local optimal value．In order to overcome these shortcomings，we pro— pose the time series gas prediction method of least squares support vector machine(L§SVM)． However，in the LS-SVM case，the sparseness and robustness may lose，and the estimation of the support values iS optimal only in the case of a Gaussian distribution of the error variables． So，this paper proposes the weighted L孓SVM tO overcome these tWO drawbacks．Meanwhile， the optimal embedding dimension and delay time of time series are obtained by the smallest dif— ferential entropy method．On this basis，multi-step time series prediction algorithm steps are given based on the weighted LS-SVM．Finally，the data of gas outburst in working face of Hebi lOth mine iS adopted to validate this model．The results show that the predict effect of shortterm the face gas emission is better using the weighted LS-SVM model than using)

文件列表：

Time Series Short-Term
......................\基于加权LS-SVM时间序列短期瓦斯预测研究.pdf,522201,2012-10-28