基于KPCA和T-S模糊神经网络的煤与瓦斯突出的预测

Prediction of Coal and Gas Outburst Based on KPCA and T-S Fuzzy Neural Network

中文关键词:  煤与瓦斯突出  核主成分分析（KPCA）  T-S模糊神经网络  仿真预测

英文关键词:coal and gas outburst  KPCA  T-S fuzzy neural network  simulation prediction

中文摘要:

      应用核主成分分析（KPCA）和T-S模糊神经网络方法对煤与瓦斯突出进行快速、精准预测。利用KPCA对实验样本数据中的多种煤与瓦斯致突因素进行降维，简化问题的复杂度，将选取的累计贡献率大于 90%的4个主成分作为T-S模糊神经网络的输入参数，煤与瓦斯突出强度作为输出参数。利用实测数据进行验证，并与BP神经网络预测模型、T-S模糊神经网络预测模型的预测结果进行比较。结果表明，该方法建立的预测模型准确性、有效性更高，收敛时间短，适用于煤与瓦斯突出预测。

英文摘要:

      The kernel principal component analysis (KPCA) and T-S fuzzy neural network methods are used to predict coal and gas outburst rapidly and accurately.The KPCA method was used to reduce the dimension and simplify the complexity of the problem in the experimental sample data.The four principal components with the cumulative contribution rate greater than 90% were selected as T-S fuzzy neural network input parameters,coal and gas outburst strength as output.The measured data were compared with the prediction results of BP neural network prediction model and T-S fuzzy neural network prediction model under the same conditions.The results show that the predictive model established by the proposed method has higher accuracy and validity,and the convergence time is shorter,which is suitable for coal and gas outburst prediction.