基于KPCA和T-S模糊神经网络的煤与瓦斯突出的预测
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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。
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.
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.
引文
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[14]付华,王馨蕊,王志军,等.基于PCA和PSO-ELM的煤与瓦斯突出软测量研究[J].传感技术学报,2014(12):1710-1715.
[15]董早鹏,刘涛,万磊,等.基于Takagi-Sugeno模糊神经网络的欠驱动无人艇直线航迹跟踪控制[J].仪器仪表学报,2015,36(4):863-870.
[2]张建民.地质因素和煤体结构对煤与瓦斯突出的影响[J]·煤矿安全,2004,35(5):43-45.
[3]郭德勇,郑茂杰,郭超,等.煤与瓦斯突出预测可拓聚类方法及应用[J].煤炭学报,2009,34(6):783-787.
[4]郭德勇,李念友,裴大文,等.煤与瓦斯突出预测灰色理论-神经网络方法[J].北京科技大学学报,2007,29(4):354-357.
[5]宁齐元,刘祖德,游曦鸣,等.基于BP神经网络煤与瓦斯突出强度预测模型[J].煤矿开采,2011,16(6):19-21.
[6]孙金根,张颖,张嘉男,等.基于BP神经网络的铝液夹杂物预测[J].控制工程,2016,23(10):1519-1522.
[7]徐哲,邬玲懿,杨成忠,等.基于BP神经网络的城市污水泵站预测模型[J].控制工程,2010,17(4):98-100.
[8]石怀涛,刘建昌,丁晓迪,等.基于混合动态主元分析的故障检测方法[J].控制工程,2012,19(1):148-151.
[9]赵洪宝,张红兵,尹光志.含瓦斯软弱煤三轴力学特性试验[J].重庆大学学报(自然科学版),2013,36(1):103-109.
[10]国家安全生产安全监督管理总局.防治煤与瓦斯突出规定[M].北京:煤炭工业出版社.2009:4-6.
[11]Qiu C Y,Xiao J,Yu L,et al.A modified interval type-2fuzzy C-means algorithm with application in MR image segmentation[J].Pattern Recognition Letters,2013,34(12):1329-1338.
[12]张子敏.瓦斯地质学[M].徐州:中国矿业大学出版社,2009.
[13]乔军伟,张光超.彝良向斜煤矿区瓦斯赋存特征[J].西安科技大学学报,2013,33(1):46-50.
[14]付华,王馨蕊,王志军,等.基于PCA和PSO-ELM的煤与瓦斯突出软测量研究[J].传感技术学报,2014(12):1710-1715.
[15]董早鹏,刘涛,万磊,等.基于Takagi-Sugeno模糊神经网络的欠驱动无人艇直线航迹跟踪控制[J].仪器仪表学报,2015,36(4):863-870.