Analog circuit fault diagnosis method based on SFO optimization depth extreme learning machine

Abstract

The invention discloses an analog circuit fault diagnosis method based on an SFO optimization depth extreme learning machine. The method comprises the following steps: inputting data; preprocessing the data; taking the training set sample as the input of a deep extreme learning machine (DELM), and training the training set sample; the method comprises the following steps: taking a test set classification error rate as a fitness function, finding a group of optimal initial weights of an extreme learning machine-based automatic encoder (ELM-AE) through a flag fish algorithm (SFO), optimizing the initial weights, and training a DELM model by using the optimized ELM-AE to enable the error rate of the DELM to be the lowest; an optimal initial weight parameter is returned through the fourth step, then the weight obtained through optimization is used for training the DELM model, and an optimal DELM model is constructed; and classifying the faults by using the optimal DELM model. Compared with the non-optimized DELM, the method has the advantages that the diagnosis accuracy of the SFO optimized DELM is improved, it is proved that the selection of hidden layer parameters affects the diagnosis precision, and the SFO algorithm has good global search capability.

Description

technical field [0001] The invention relates to the field of analog circuit fault diagnosis, in particular to an analog circuit fault diagnosis method based on Sailfish algorithm (SFO) optimization deep extreme learning machine. Background technique [0002] With the wide application of electronic systems in military, communications, aerospace, medical, household appliances and other fields, improving the stability, safety and maintainability of electronic systems has become a basic issue in the field of circuits. Analog circuits are an important part of electronic systems and play a vital role in the reliable operation of electronic systems. However, circuit failure may lead to a decline in the performance of the machine, and in severe cases, it will cause an emergency shutdown, endangering people's lives and property safety. Therefore, it is very important to use fault diagnosis of analog circuits to improve the safety and reliability of electronic systems, and developing...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems of high feature similarity between fault categories in analog circuit fault diagnosis and difficulty in selecting hidden layer parameters of extreme learning machines, the present invention provides an analog circuit fault diagnosis method based on SFO optimized deep extreme learning machine

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