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Inversion technology of transitional history of negative step-force based on neural networks

机译:基于神经网络的负阶跃力过渡历史反演技术

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In the paper, a kind of new metrology of negative step-force is put forwards based on neural networks. Firstly, the dynamometric system of negative step-force generator is introduced which contains a special design of measuring cell that is connected in series with a force transducer. Secondly, a neural network model between the negative step-force and the displacement response of a certain point on the surface of the cell is built based on the technology of finite element analysis. Then according to the displacement response of the point on the cell, the dynamic force that acts on the transducer can be inversed, and the method proves to be true theoretically. Finally, a kind of laser Doppler interferometer is designed to measure the velocity of the point on the surface of the cell, and the displacement is calculated in the meantime, and then the transitional history of negative step-force could be got based on the neural network model, and the method proves to be correct experimentally.
机译:本文提出了一种基于神经网络的新型负阶跃力测量方法。首先,介绍了负步进力发生器的测力系统,该系统包含与测力传感器串联的特殊设计的测量单元。其次,基于有限元分析技术,建立了负阶跃力与细胞表面某点位移响应之间的神经网络模型。然后根据点在单元格上的位移响应,可以反作用于换能器上的动力,该方法在理论上被证明是正确的。最后,设计了一种激光多普勒干涉仪来测量细胞表面点的速度,并同时计算位移,然后可以基于神经网络获得负阶跃力的跃迁历史。网络模型,实验证明该方法是正确的。

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