极限学习机与支持向量机在储层渗透率预测中的对比研究  

Comparison of the Extreme Learning Machine with the Support Vector Machine for Reservoir Permeability Prediction

作　　者：潘华贤[1] 程国建[1] 蔡磊[1] 

机构地区：[1]西安石油大学计算机学院,陕西西安710065

出　　处：《计算机工程与科学》2010年第2期131-134,共4页Computer Engineering & Science

基　　金：国家自然科学基金资助项目(40872087)

摘　　要：极限学习机ELM是一种简单易用、有效的单隐层前馈神经网络SLFNs学习算法。传统的神经网络学习算法(如BP算法)需要人为设置大量的网络训练参数,并且很容易产生局部最优解。极限学习机只需要设置网络的隐层节点个数,在算法执行过程中不需要调整网络的输入权值以及隐元的偏置,并且产生唯一的最优解,因此具有学习速度快且泛化性能好的优点。本文将极限学习机引入到储层渗透率的预测中,通过对比支持向量机,分析其在储层渗透率预测中的可行性和优势。实验结果表明,极限学习机与支持向量机有近似的预测精度,但在参数选择以及学习速度上极限学习机具有明显的优势。Extreme Learning Machine （ELM） is an easy to-use and effective learning algorithm of single-hidden layer feedforward neural networks （SLFNs）. The classical learning algorithm in neural networks, e. g. back propagation, requires to set several user-defined parameters and may produce the local minimum. However, the extreme learning machine only requires to set the number of hidden neurons and the activation function. It does not need to adjust the input weights and hidden layer biases during the implementation of the algorithm, and it produces only one optimal solution. Therefore, ELM has the advantages of fast learning speed and good generalization performance. In this paper, ELM is introduced in predicting reservoir permeability, and by comparing with SVM, we analyse its feasibility and advantages in reservoir permeability prediction. The experimental results show that ELM has similar accuracy compared to SVR, but it has obvious ad- vantages in parameter selection and learning speed.

关 键 词：极限学习机 前馈神经网络 渗透率 支持向量机 预测模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]