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基于混沌RBF神经网络的气化炉温度软测量系统
引用本文:王学武,王冬青,陈程,顾幸生,孙自强. 基于混沌RBF神经网络的气化炉温度软测量系统[J]. 化工自动化及仪表, 2006, 33(5): 48-50,54
作者姓名:王学武  王冬青  陈程  顾幸生  孙自强
作者单位:华东理工大学,自动化研究所,上海,200237;兖矿集团鲁南化肥厂,山东,滕州,277527
摘    要:针对德士古气化炉炉膛温度难以测量这一情况,提出利用软测量技术来解决这一问题.通过建立BP网络模型和RBF网络模型以及基于PCA和CHAOS的神经网络模型,并对其仿真结果进行分析和比较,验证了该方法的可行性.CHAOS-RBF软测量模型在化肥厂的应用效果良好,误差保持在1.5%以内,不但提高了温度测量精度,而且有利于更好的生产控制.

关 键 词:软测量  BP网络  RBF网络  主元分析  混沌
文章编号:1000-3932(2006)05-0048-03
收稿时间:2006-05-08
修稿时间:2006-05-08

Soft-sensing System of Gasification Furnace Temperature Based on Chaos-RBF Neural Network
WANG Xue-wu,WANG Dong-qing,CHEN Cheng,GU Xing-sheng,SUN Zi-qiang. Soft-sensing System of Gasification Furnace Temperature Based on Chaos-RBF Neural Network[J]. Control and Instruments In Chemical Industry, 2006, 33(5): 48-50,54
Authors:WANG Xue-wu  WANG Dong-qing  CHEN Cheng  GU Xing-sheng  SUN Zi-qiang
Affiliation:1. Research Institute of Automation,East China University of Science and Technology,Shanghai 200237 ,China; 2. Lunan Fertilizer Plant, Yankuang Group, Tengzhou 277527, China
Abstract:Aiming at the difficulty in measuring the temperature of Texaco gasification furnace, soft-sensing method is applied to solve this question. The BP network model, RBF network model and ANN model based on PCA and CHAOS are established. The simulation results are analyzed and compared, practicability of this method is proved. CHAOS-RBF soft-sensing model is applied in Lunan fertilizer plant;temperature error is maintained below 1.5 percent.
Keywords:soft-sensing   BP network   RBF network   PCA   CHAOS
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