引用本文:褚菲,王福利,王小刚,张淑宁.基于径向基函数神经网络的多级离心压缩机混合模型[J].控制理论与应用,2012,29(9):1205~1210.[点击复制]
CHU Fei,WANG Fu-li,WANG Xiao-gang,ZHANG Shu-ning.Hybrid model for multi-stage centrifugal compressor based on radial basis function neural network[J].Control Theory and Technology,2012,29(9):1205~1210.[点击复制]
基于径向基函数神经网络的多级离心压缩机混合模型
Hybrid model for multi-stage centrifugal compressor based on radial basis function neural network
摘要点击 2001  全文点击 1809  投稿时间:2011-09-29  修订日期:2012-02-27
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DOI编号  
  2012,29(9):1205-1210
中文关键词  离心压缩机  性能预测  混合模型  径向基函数神经网络  非线性  能量损失机理
英文关键词  centrifugal compressor  performance prediction  hybrid model  radial basis function neural network  nonlinearities  loss mechanism
基金项目  国家自然科学基金资助项目(61074074, 61174130, 61004083); 国家“863”计划资助项目(2011AA060204); 国家“973”计划子课题资助项目(2009CB320601).
作者单位E-mail
褚菲* 东北大学 流程工业综合自动化国家重点实验室 chufeizhufei@sina.com 
王福利 东北大学 流程工业综合自动化国家重点实验室  
王小刚 东北大学 流程工业综合自动化国家重点实验室
东北大学 信息科学与工程学院 
 
张淑宁 东北大学 流程工业综合自动化国家重点实验室  
中文摘要
      大型离心压缩机作为多影响因素和强非线性的复杂系统, 其性能的准确预测难以实现. 针对这一问题, 结合径向基函数(RBF)神经网络, 本文建立了多级离心压缩机性能预测的混合模型. 首先基于热力学第一定律和压缩机能量损失机理建立了多级离心压缩机性能预测的机理模型. 该模型无需任何实验确定的性能曲线, 完全由压缩机的几何结构参数预测出压缩机在设计工况和非设计工况下的性能. 然后利用RBF神经网络修正机理模型的误差, 并通过对RBF神经网络的不断更新, 进一步提高了模型的预测精度和适用性. 将所建立的混合模型应用于实际的离心压缩机, 结果表明该方法具有良好的预测性能.
英文摘要
      The large centrifugal compressor is a complex system with many factors and strong nonlinearities; the performance of which cannot be predicted accurately. To deal with this problem, we propose a hybrid model for predicting the performance of a multistage centrifugal compressor by employing the radial basis function (RBF) neural network. First, according to the structural parameters of the compressor instead of the experimental characteristic, we deduce a theoretical prediction model based on the first law of thermodynamics and the energy loss mechanism. This model is used to predict the design performance and the off-design performance of the compressor. Then, a RBF neural network, which is updated in time, is applied to the theoretical model to form a hybrid model, in which the error of the theoretical model is continuously corrected to raise its accuracy in the process of performance prediction. This hybrid model has been used to predict the performance of practical multistage centrifugal compressors in industrial applications; the results of performance prediction are satisfactory