Analog circuit fault diagnosis method based on improved RBF neural network

A technology for simulating circuit faults and neural networks, applied in the field of analog circuit fault diagnosis, it can solve the problems of complex feedback loop simulation, non-linear simulation circuit, and increase network complexity, so as to reduce the number of iterations, improve the recognition rate, and reduce errors. Effect

Inactive Publication Date: 2014-07-16
CHONGQING UNIV
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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

Method used

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  • Analog circuit fault diagnosis method based on improved RBF neural network
  • Analog circuit fault diagnosis method based on improved RBF neural network
  • Analog circuit fault diagnosis method based on improved RBF neural network

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Embodiment Construction

[0027] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0028] In the invention, the extraction of candidate fault feature vectors based on wavelet packet transform is used to improve the resolution of faults; the fault features are formed through preprocessing such as normalization, which effectively eliminates the original variables due to different dimensions and large numerical differences. The extraction of fault features is realized; by using the genetic optimization algorithm to replace the LMS method (minimum mean square error method) in the RBF algorithm to train the parameters of the neural network (weights and thresholds, etc.), the performance of the RBF algorithm can be improved. At the same time, the K-means clustering learning algorithm is used to set the optimal starting point of the genetic algorithm, which effectively reduces the number of iterations of the algorithm, reduces er...

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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...

Claims

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Application Information

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IPC IPC(8): G01R31/316
Inventor 魏善碧柴毅邓萍陈淳王诗年唐健
Owner CHONGQING UNIV
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