计算机工程与应用Issue(10)：52-56,5.DOI:10.3778/j.issn.1002-8331.1308-0413
改进PSO-BP神经网络对储层参数的动态预测研究
Dynamic prediction on reservoir parameter by improved PSO-BP neural network
摘要
Abstract
In order to improve the convergence speed and generalization ability of BP neural network and prevent it from falling into local optimal value, the traditional particle swarm optimization algorithm is improved in three aspects based on the previous research, including the limit of the maximum speed, the changes of the inertia weight factor and the improve-ment of the fitness function. Then it is used to optimize the weight and threshold of the BP neural network. And the dynamic prediction on reservoir parameter is realized by the improved PSO-BP neural network, the whole process is determining the input and output neurons, quantitating the time parameter, constructing the neural network model with the training samples and testing it. Finally, the simulation results of the average training error is analyzed, and it proves that the conver-gence and generalization ability of the improved PSO-BP algorithm are better than the BP algorithm and PSO-BP algorithm.关键词
改进PSO-BP神经网络/惯性权重因子/储层参数/预测Key words
improved PSO-BP neural network/inertia weight factor/reservoir parameter/predication分类
信息技术与安全科学引用本文复制引用
潘少伟,梁鸿军,李良,王家华..改进PSO-BP神经网络对储层参数的动态预测研究[J].计算机工程与应用,2014,(10):52-56,5.基金项目
陕西省自然科学基金（No.2012JQ8040，No.2012JM8037）；陕西省教育厅科学研究计划项目（No.2013JK1134）。 （）
