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基于遗传算法的神经网络油水层识别
引用本文:曾黄麟,李娟. 基于遗传算法的神经网络油水层识别[J]. 四川轻化工学院学报, 2010, 0(5): 590-593
作者姓名:曾黄麟  李娟
作者单位:[1]四川理工学院自动化与电子信息学院,四川自贡643000 [2]山西工商职业学院计算机系,太原030000
基金项目:国家高技术研究发展计划(863计划)第四批课题(2008AA11A134)
摘    要:文章研究了基于遗传算法的神经网络油水层识别方法,针对神经计算存在因输入信息空间维数较大而使网络结构复杂、训练时间长,以及因冗余属性使网络拟合精度不高等缺点,提出了基于粗集属性约简方法降低了输入信息的空间维数、减少了运算量和简化了神经网络的拓扑结构,利用遗传算法提高神经网络的训练速度。实验结果表明:将混合智能计算方法应用于油水层识别中效果显著,其学习训练速度和拟合精度远优于传统BP神经网络算法。

关 键 词:属性约简  神经网络  遗传算法  油水层识别

An Oil-water Layer Recognition Based on a Genetic Algorithm Neural Network
ZENG Huang-lin,LI Juan. An Oil-water Layer Recognition Based on a Genetic Algorithm Neural Network[J]. Journal of Sichuan Institute of Light Industry and Chemical Technology, 2010, 0(5): 590-593
Authors:ZENG Huang-lin  LI Juan
Affiliation:1.School of Automation and Electronic Information,Sichuan University of Science &Engineering,Zigong 643000,China;2.Department of Computer Science,Shanxi College of Bussiness and Technology,Shanxi 030000,China)
Abstract:In this paper,a composition intelligence computing method is suggested for an oil-water layer recognition.The redundant condition attributes are reduced based on rough set attribute simplification algorithm so that an oil-water layer neural network recognition system can be simplified to improve network composition.An optimization computation speed of neural network is improved by a BP learning with a genetic algorithm.Simulation result shows that the effect in oil-water layer recognition is improved by the composition intelligence computing method proposed here.
Keywords:attribute reduction  neural network  genetic algorithm  oil-water layer recognition
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