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- W2392993008 abstract "The flowrate signal is an important parameter in thermal processes.However,flowrate signals generally contain nonlinearity and randomicity,it is difficult to build accurate models by traditional methods such as the three approximate mathematical models of the velocity distribution of turbulent flow in cylindrical pipes,and so the traditional method of flowrate measurement based on these models was impossible to get accurate results.A novel RBF neural network model is presented in this paper.A gradient descend algorithm with a momentum factor in this network model is introduced to decide the positions of hidden layer RBF centers and output layer weights.The soft-sensing model is established with the novel RBF neural network,the computational results show that the model has less calculation and high precision,and the relevant algorithm is very simple and useful.The experimental results show clearly that the measurement precision of flowrate is greatly improved comparing with traditional methods." @default.
- W2392993008 created "2016-06-24" @default.
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- W2392993008 date "2006-01-01" @default.
- W2392993008 modified "2023-09-25" @default.
- W2392993008 title "Study on Soft-sensing Model of the Gas Flowrate Measurement Based Upon RBF Neural Network" @default.
- W2392993008 hasPublicationYear "2006" @default.
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