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  

Self-organizing Map Algorithm Based on Intra-class Minimum Similarity Degree and Application in Reservoir Prediction

作　　者：鲍彬彬[1] 吴清强[1] 

机构地区：[1]厦门大学软件学院,福建厦门361005

出　　处：《厦门大学学报（自然科学版）》2017年第3期437-441,共5页Journal of Xiamen University：Natural Science

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 al-so 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 self-organizing map algorithm based on intra-class minimum similarity degree （SOM-IMSD）, which introduce intra-class similaritydegree into the process of SOM neural network learning.Adjust IMSD to guide SOM neural network learning,which makes the aver-age IMSD maximum and improves the quality of cluster result. Apply the SOM-IMSD and basic SOM to reservoir prediction andcompare the results.The experiment shows that it has improved the clustering results.

关 键 词：自组织映射 类内最小相似度 储层预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]