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Error correction of a coriolis mass flow meter in two-phase flow measurment using Neuro-Fuzzy

机译:用神经模糊纠正两相流量测量中的科里奥利质量流量计

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Coriolis mass flow meters despite proper performance and precision when faced with two-phased fluids will show a remarkable attenuation in performance and accuracy depending on the condition and the amount of variation of the density of passing fluid. In these situations Neuro-Fuzzy Algorithm helps determination of the error and improves performance and also looks a better choice compared to specific transmitters according to reduction in cost. Design and simulation have been made through MATLAB software. Previous measures made in this area encompassed using Neural Network [1] that this work was based on laboratory data and evidences in that research.
机译:Coriolis质量流量计尽管有适当的性能和精度,当面对双相流体时,可显着的性能和准确性衰减,这取决于通过流体密度的变化和变化量。在这些情况下,神经模糊算法有助于确定误差并提高性能,并且与根据成本的降低相比,与特定发射器相比也更好地选择。通过Matlab软件进行了设计和仿真。以前在该领域制造的措施包括神经网络[1]这项工作基于该研究的实验室数据和证据。

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