控制理论与应用2012，Vol.29Issue(9)：1205-1210,6.
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
Abstract
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Key words
centrifugal compressor/ performance prediction/ hybrid model/ radial basis function neural network/ non-linearities/ loss mechanism分类
信息技术与安全科学引用本文复制引用
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):1205-1210,6.基金项目
国家自然科学基金资助项目(61074074,61174130,61004083) （61074074,61174130,61004083）
国家"863"计划资助项目(2011AA060204) （2011AA060204）
国家"973"计划子课题资助项目(2009CB320601). （2009CB320601）
