Method for diagnosing soft failure of analog circuit base on modified type BP neural network

Abstract

The invention relates to a method which can diagnose the soft fault of an analog circuit based on an improved BP neural network. The method comprises the following steps that: the excitation signal and the test node of the analog circuit are selected by adopting the random sampling technique, then a circuit to be tested is applied with the excitation signal and the voltage value is extracted at the test node, and then through principal component analysis and the normalization processing, the characteristic value of the soft fault is extracted and is taken as a training sample; the BP network is optimized by adopting the immune genetic algorithm; the training sample is input into the optimized BP network to realize the training to the network; the practical measured signal of the circuit to be tested is input into the trained optimal BP neural network after being extracted with fault characteristics, and the output of the network is of a fault type. The invention effectively processes the fault diagnosis difficulty of the analog circuit, which is brought out by the tolerance, and improves the efficiency and the performance of the BP network in the analog circuit fault diagnosis.

Description

technical field [0001] The invention relates to a method for diagnosing a soft fault of an analog circuit, in particular to a method for diagnosing a soft fault of an analog circuit based on an improved BP neural network. Background technique [0002] With the rapid development of the electronics industry, the scale of modern engineering technology systems continues to expand, and electronic products are increasingly large-scale, high-speed, automated and intelligent. People are increasingly aware that the reliability of the electronic system is the guarantee of the stable operation of the system. In many cases, its importance even exceeds the function and performance of the system. In , the analog circuit cannot be completely replaced. According to statistics, the proportion of analog circuits and digital circuits has not changed much over the years, and analog circuits are more prone to failure than digital circuits. Therefore, industrial production has put forward new an...

AI Technical Summary

Problems solved by technology

[0005] Sensitivity analysis of the circuit to be tested is an effective method to determine the test nodes and excitation signals of the circuit, but the existing sensitivity analysis has many constraints, such as it can only be used to predict the impact on network performance when there are small changes in network parameters , but cannot correctly give the change of the network performance function when the network parameters have a large change

The weight adjustment of the BP neural network is done through the traditional BP algorithm, which is a gradient-based search algorithm, which usually has the disadvantages of low learning efficiency, slow convergence speed, and easy to fall into local optimum.

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