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水泥分解炉运行参数预测模型
引用本文:周元,彭钟钟,黄良沛.水泥分解炉运行参数预测模型[J].哈尔滨理工大学学报,2010,15(6):86-90.
作者姓名:周元  彭钟钟  黄良沛
作者单位:湖南科技大学机械设备健康维护湖南省重点实验室,湖南湘潭411201
基金项目:国家863计划资助项目,湖南省教育厅科学研究项目
摘    要:为了实现水泥分解炉非线性系统的建模与控制,利于数学分析等特点的T-S型模糊推理与可以实现任意非线性映射的神经网络相结合,在水泥分解炉生产工艺分析的基础上,建立了水泥分解炉运行参数的模糊神经网络预测模型.该预测模型结合某大型水泥厂现场采集的生产数据,进行了仿真验证.结果表明,模型计算简单,模型网络运算输出值(预测值)与样本期望值(现场采集数据)相差很小,说明该模糊神经网络具有较好的预测能力和泛化能力.

关 键 词:分解炉  T-S型模糊推理  神经网络  预测  运行参数

Forecast Model of Cement Decomposing Furnace Performance Parameters
ZHOU Yuan,PENG Zhong-zhong,HUANG Liang-pei.Forecast Model of Cement Decomposing Furnace Performance Parameters[J].Journal of Harbin University of Science and Technology,2010,15(6):86-90.
Authors:ZHOU Yuan  PENG Zhong-zhong  HUANG Liang-pei
Affiliation:(Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment,Hunan University ofScience and Technology,Xiangtan 411201,China)
Abstract:To obtain the modeling and control for nonlinearity system of cement decomposing furnace,T-S fuzzy inference where mathematics analysis is easy is combined with neural network which has random non-linear mapping characteristics.Based on analysis of production technology for decomposing furnace,a fuzzy neural network forecast model of decomposing furnace performance parameters is established.The model is simulated with the data collected online from a large-scale cement plant.The simulation shows that the model calculation method is simple,the output(forecast value) is close to the expectation value(data collected online),and the fuzzy neural network has good ability of forecast and generalization.
Keywords:decomposing furnace  T-S fuzzy inference  neural network  forecast  performance parameter
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