Fault Diagnosis of Support Vector Machine Analog Circuits Based on Improved Particle Swarm Optimization

The development of electronic circuits requires that the reliability and security of circuit equipment and system operation are also increasing. In addition, due to the complexity of the operating environment, it is very important to strengthen the fault diagnosis and real-time testing technology of analog circuits in circuit systems. Based on this, this paper studied the fault diagnosis of analog circuits with Support Vector Machine (SVM), and introduced Improved Particle Swarm Optimization (IPSO) algorithm to optimize the parameters of SVM. In other words, the dynamic weight setting and factor improvement of Particle Swarm Optimization (PSO) algorithm aim to accelerate algorithm performance improvement, and information extraction and diagnosis model construction are carried out on the basis of considering circuit fault characteristics. Through the performance test and application analysis of the improved algorithm proposed in the study, the error value of the improved algorithm was basically stable at 0.0103 in the late stage of classification training, and its prediction accuracy rate was more than 80%, and the classification consumption time was less. At the same time, the accuracy of fault feature extraction results in training and test scenarios was above 94%, and the search performance was obviously better than other comparison algorithms, which effectively improved the fault diagnosis accuracy and efficiency. The IPSO algorithm model can effectively identify analog circuit fault information, and shows good information optimization performance. It has certain validity and rationality in circuit fault diagnosis and security assurance.