大庆石油学院学报2011，Vol.35Issue(6)：76-79,84,5.
一种面向系统状态参数预报的过程神经网络模型及其算法
摘要
Abstract
Aiming at condition prediction problems of nonlinear system that are difficult to be describedwith definite mechanism model, a prediction model and method based on process neural network (PNN) is proposed in the paper. Using the nonlinear mapping mechanism to dynamic system and direct identification modeling ability of PNN, oriented to system state parameter prediction, a process neural network model is built that can reflect modal characteristic change of system process, its prediction mechanism is analyzed, and the learning algorithm is given. At the same time, in order to make up for the deficiency of real sampling data and improve information use ratio, the training sample set of PNN is constructed u-sing phase space reconstruction. Taking state change prediction of well group oil recovery rate in oil field development as example, experiment results proved the effectiveness of the model and the algorithm.关键词
非线性系统/状态预报/过程神经网络/学习算法/采油速度Key words
nonlinear system/ condition prediction/ process neural network/ learning algorithm/ recovery rate分类
计算机与自动化引用本文复制引用
庞跃武,许少华..一种面向系统状态参数预报的过程神经网络模型及其算法[J].大庆石油学院学报,2011,35(6):76-79,84,5.基金项目
国家自然科学基金(60473051) （60473051）
中国石油科技创新基金(2010D-5006-0302) （2010D-5006-0302）
