基于类内最小相似度自组织映射算法及其在储层预测中的应用
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摘要
为了解决自组织映射(Self-organization map,SOM)神经网络算法部分神经元过度利用和欠利用的问题,提出基于类内最小相似度的SOM算法(SOM based on intraclass minimun similarity degree,SOM-IMSD),将类内相似度这一评价指标引入SOM神经网络学习过程中,通过调整类内最小相似度来指导SOM神经网络学习,使得平均类内最小相似度最大,提高SOM神经网络的聚类结果质量.将SOM-IMSD算法应用于储层预测,并与基本SOM算法进行对比,实验结果表明,SOM-IMSD算法的聚类结果更为准确.
Intra-Class similarity degree is a commonly used evaluation index to evaluate the quality of the clustering results.It can also be used to weigh the cluster result.In order to solve the problem of excessive use and less use of some neurons,we propose a selforganizing map algorithm based on intra-class minimum similarity degree(SOM-IMSD),which introduce intra-class similarity degree into the process of SOM neural network learning.Adjust IMSD to guide SOM neural network learning,which makes the average IMSD maximum and improves the quality of cluster result.Apply the SOM-IMSD and basic SOM to reservoir prediction and compare the results.The experiment shows that it has improved the clustering results.
Intra-Class similarity degree is a commonly used evaluation index to evaluate the quality of the clustering results.It can also be used to weigh the cluster result.In order to solve the problem of excessive use and less use of some neurons,we propose a selforganizing map algorithm based on intra-class minimum similarity degree(SOM-IMSD),which introduce intra-class similarity degree into the process of SOM neural network learning.Adjust IMSD to guide SOM neural network learning,which makes the average IMSD maximum and improves the quality of cluster result.Apply the SOM-IMSD and basic SOM to reservoir prediction and compare the results.The experiment shows that it has improved the clustering results.
引文
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[2]张静,王志章,赵永军,等.支持向量机在致密砂岩储层孔隙度预测中应用[J].河南科学,2015(6):993-997.
[3]杜波,于正军,韩建军.多元线性回归法在DX北带砂砾岩储层孔隙度定量预测中的应用[J].天然气勘探与开发,2012,35(4):36-40.
[4]刘正锋,燕军.模糊识别方法在储层识别中的应用[J].西南石油大学学报(自然科学版),1998,20(3):4-6.
[5]刁凤琴,诸克君,於世为.一种优化的BP神经网络算法在石油储层预测中的应用[J].系统管理学报,2008,17(5):499-503.
[6]杨黎刚,苏宏业,张英.基于SOM聚类的数据挖掘方法及其应用研究[J].计算机工程与科学,2007,29(8):133-136.
[7]杨占华,杨燕.SOM神经网络算法的研究与进展[J].计算机工程,2006,32(16):201-202.
[8]DE A,GUO C.A vector quantization approach for image segmentation based on SOM neural network[C]∥International Conference on Advances in Neural Networks.Berlin,Heidelberg:Springer-Verlag,2013:612-619.
[9]LIU F,ZHANG G L,SUN Y J,et al.Mapping the threedimensional distribution of soil organic matter across a subtropical hilly landscape[J].Soil Science Society of America Journal,2013,77(4):1241-1253.
[10]SUN A X.Improved SOM algorithm-hdsom applied in text clustering[C]∥2010International Conference on Multimedia Information Networking and Security.Piscataway:IEEE,2010:42-44.
[11]AKINDUKO A A,MIRKES E M,GORBAN A N.SOM:stochastic initialization versus principal components[J].Information Sciences,2015,364/365:213-221.
[12]陈善学,杜峰,吴立彬.一种改进的等误差自组织特征映射矢量量化算法[J].重庆邮电大学学报(自然科学版),2011,23(2):155-160.
[13]于鷃.基于一维SOM神经网络的聚类及数据分析方法研究[D].天津:天津大学,2009:1.
[14]杨培杰,印兴耀,张广智.模糊C均值地震属性聚类分析[J].石油地球物理勘探,2007,42(3):322-324.
[15]TURKAY C,PARULEK J,REUTER N,et al.Integrating cluster formation and cluster evaluation in interactive visual analysis[C]∥Spring Conference on Computer Graphics.New York:ACM,2011:77-86.
[16]杨燕,靳蕃,KAMEL M.聚类有效性评价综述[J].计算机应用研究,2008,25(6):1630-1632.
[17]刘燕驰,高学东,国宏伟,等.聚类有效性的组合评价方法[J].计算机工程与应用,2011(19):15-16.