BP改进算法及其在乙二醇精制软测量中的应用  

An Improved BP Algorithm and Its Application in Soft Sensing of Purifying Ethylene Glycol

作　　者：张磊[1] 胡春[1] 钱锋[1] 

机构地区：[1]华东理工大学自动化研究所,上海200237

出　　处：《自动化仪表》2005年第6期31-34,共4页Process Automation Instrumentation

摘　　要：提出了一种综合改进的BP神经网络算法,该算法在训练时对不同的连接权和阈值采用不同的学习速率,由此建立了乙二醇精制塔塔釜乙二醇浓度的神经网络软测量模型。结果表明该算法能有效提高乙二醇浓度BP神经网络软测量模型的收敛精度。A comprehensively improved BP algorithm is issued in this paper. According to this algorithm, different learning rates are applied to different connection weights and threshold values when a BP network is trained. A neural network model is setup to be used on soft sensing of the concentration of the ethylene glycol of the MEG column. Practice shows that this proposed algorithm is effectively able to improve the convergence precision of the BP neural network in soft sensing for the concentration of ethylene glycol.

关 键 词：乙二醇 改进算法 BP神经网络算法 软测量模型 应用 综合改进 学习速率 收敛精度 醇浓度 连接权 精制塔 阈值 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] TQ223.162[自动化与计算机技术—计算机科学与技术]