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Fault diagnosis of VNA intermediate frequency processing system based on dynamic fuzzy neural network

机译:基于动态模糊神经网络的VNA中频处理系统故障诊断

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The paper presents a new fault diagnosis method for the intermediate frequency (IF) signal processing system of the vector network analyzer (VNA) based on dynamic fuzzy neural network (DFNN). This paper gives the structure of the fault diagnosis with three test points in one port first. Then for four different ports, it chooses the same method. The fault diagnosis is done by on-line self-organizing DFNN, and the structure and parameter identification is made in the on-line process without default values of structure and network parameters. It is the first time to introduce the DFNN into the fault diagnosis of the VNA IF system. Finally, the simulation experiment shows that the method can well approximate the nonlinear feature of the fault, and it is effective for fault diagnosis.
机译:提出了一种基于动态模糊神经网络(DFNN)的矢量网络分析仪(VNA)中频(IF)信号处理系统故障诊断方法。本文首先给出了在一个端口上具有三个测试点的故障诊断的结构。然后,对于四个不同的端口,它选择相同的方法。故障诊断是通过在线自组织DFNN进行的,结构和参数的识别是在在线过程中进行的,没有结构和网络参数的默认值。这是第一次将DFNN引入VNA IF系统的故障诊断中。最后,仿真实验表明,该方法能够很好地近似故障的非线性特征,对故障诊断是有效的。

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