Analog circuit fault diagnosis method based on improved RBF neural network

Abstract

The invention discloses an analog circuit fault diagnosis method based on an improved RBF neural network. The analog circuit fault diagnosis method includes the following steps that excitation is exerted on a circuit to be detected, and response signals are processed through improved wavelet packet transformation to extract fault characteristic signals; the extracted candidate characteristic signals are normalized to obtain fault characteristic vectors; the fault characteristic vectors serving as samples are input into the neural network and classified to obtain a result of fault diagnosis. Extraction of the fault characteristic vectors based on wavelet packet transformation is adopted, so that the distinguishability is improved; through normalization and other preprocessing, influences caused by different dimensions and too large numerical value difference on original variables are effectively eliminated; an LMS method in an RBF algorithm is replaced by a genetic optimization algorithm to train parameters of the neural network, so that the performance of the RBF algorithm is improved, an optimizing starting point of a genetic algorithm is set through a K average clustering learning algorithm, the iterations of the algorithm is effectively reduced, errors are reduced, diagnosis speed is increased, and the fault recognition rate is improved.

Description

technical field [0001] The invention belongs to the field of analog circuit fault diagnosis and relates to an analog circuit fault diagnosis method based on an improved RBF neural network. Background technique [0002] In today's information age, electronic systems such as computers, communications, and automation systems are closely related to industrial production and daily life, and circuits are the hardware foundation of electronic systems. Once the circuit fails, the electronic system and even the entire system will not work properly. At present, electronic devices are closely related to people's lives and have been widely used in various fields. And its operating environment is diverse, from the ordinary living environment of human beings to the harsh or even very harsh environments where human beings cannot survive, such as ultra-high temperature, ultra-low temperature, high humidity, nuclear radiation, high electromagnetic field and other environments. With the wide...

AI Technical Summary

Problems solved by technology

[0006] (3) Nonlinear problems widely exist in analog circuits;

[0007] (4) The actual number of measurable nodes is limited;

[0008] (5) Feedback loops in practical circuits lead to complex simulations

But the result can lead to network redundancy, increasing the complexity of the network

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