基于遗传优化径向基概率神经网络的岩性识别应用  

Lithology identification based on genetic optimized radial basis probabilistic neural network

作　　者：靳玉萍[1] 李保霖[1] 

机构地区：[1]西安科技大学计算机科学与技术学院,西安710054

出　　处：《计算机应用》2013年第2期353-356,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(51134019)

摘　　要：岩性识别是测井数据解释中最关键的一环,但传统的岩性识别方法解释效率慢,精度低,受人为因素影响大。为此,提出一种遗传优化径向基概率神经网络(RBPNN)的岩性识别方法。该方法融合概率神经网络(PNN)和径向基函数神经网络(RBFNN)的优势来构造RBPNN,采用遗传算法搜索使得RBPNN训练法误差最小的最优隐中心矢量和相匹配的核函数控制参数,优化网络结构,提高收敛速度与精度,形成全结构遗传优化的RBPNN模型。实例应用表明,基于遗传优化RBPNN的岩性识别能够达到工程实际应用的规范标准,且是可行有效的,能够为油田地质勘探领域的岩性识别提供科学的理论支持与依靠。Lithology identification is the most critical procedure in the logging data interpretation field, while the traditional lithology identification methods have a lot of defects such as slow explain efficiency, low accuracy, and big influenced human factors. To resolve these problems, a new kind lithology identification method was put forward using genetic optimized Radial Basis Probability Neural Network （RBPNN）. Probabilistic Neural Network （PNN） and the Radial Basis Function Neural Network （RBFNN） were combined to construct RBPNN. To optimize network structure, upgrade convergence speed and accuracy, Genetic Algorithm （GA） was used to search for the optimal hidden center vector and matching kernel function control parameters of the RBPNN structure which must satisfy minimum error of RBPNN training and form genetic optimized RBPNN network model. The case study shows that lithology identification based on genetic optimized RBPNN can achieve the actual application standards, and it is feasible and effective, it also can provide scientific theoretical supports and dependences for oil geological exploration field.

关 键 词：岩性识别 径向基概率神经网络 遗传算法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]