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A kind integrated adaptive fuzzy neural network tolerance analog circuit fault diagnosis method

机译:一种综合的自适应模糊神经网络容差模拟电路故障诊断方法

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Combining fuzzy theory and neural network is an effective way to be applied in fault diagnosis of analog circuit. For tolerance analog circuit fault, this paper proposed a kind new based on integrated adaptive fuzzy neural network the diagnosis method. The method first uses wavelet transform to extract the signal from the output sample, and characteristics of fault feature vectors are normalized. Then it uses the principal element analysis to reduce the fault sample dimension, the network architecture can be simplified, the computation complexity can be reduced. Afterward training and testing integrated adaptive fuzzy neural network with the preprocessed fault characteristic data. Experimentation indicates that the method has higher diagnosis nicety rate and effectively solves fault tolerance of ambiguity and problems.
机译:将模糊理论与神经网络相结合是一种有效的模拟电路故障诊断方法。针对容错模拟电路故障,本文提出了一种基于集成自适应模糊神经网络的新型诊断方法。该方法首先使用小波变换从输出样本中提取信号,并对故障特征向量的特征进行归一化。然后利用主元分析来减小故障样本的维数,简化网络架构,降低计算复杂度。之后,通过预处理的故障特征数据对集成的自适应模糊神经网络进行训练和测试。实验表明,该方法具有较高的诊断满意率,有效解决了模棱两可和问题的容错性。

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