Optimized SVM-based analog circuit fault diagnosis method and system, and storage medium
By combining the particle swarm algorithm and the improved Gray Wolf algorithm to optimize the parameters of SVM, the PSO-IGWO-SVM model is built, which solves the problems of low diagnostic accuracy and inaccurate parameter optimization in analog circuit fault diagnosis, and achieves more efficient and accurate fault diagnosis.
CN119667453APending Publication Date: 2025-03-21HEBEI CREATE INSTR TECH CO LTD
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Patent Information
- Application Number
- CN202411447879.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-17
- Publication Date
- 2025-03-21
AI Technical Summary
Technical Problem
The prior art has low diagnostic accuracy and low efficiency in analog circuit fault diagnosis, and the SVM parameter optimization is inaccurate, which affects the diagnostic effect.
Method used
Using a method combining particle swarm algorithm with improved gray wolf algorithm, the kernel function g and punishment factor C of SVM are optimized to build a PSO-IGWO-SVM model to improve diagnostic accuracy and efficiency.
Benefits of technology
Through algorithm fusion, the classification effect and diagnostic accuracy of the SVM model are improved, the search efficiency and robustness of the algorithm are enhanced, and the problem of inaccurate parameter optimization in traditional methods is solved.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses an analog circuit fault diagnosis method and system for optimizing an SVM (Support Vector Machine) and a storage medium. An SVM algorithm is optimized by adopting a fused particle swarm algorithm and an improved grey wolf algorithm. Comprising the following steps: extracting time domain features and wavelet packet features of various fault sample signals of an analog circuit, and fusing the two features as a final fault feature sample set; dividing the fault feature sample set into a training set and a test set according to a certain proportion; optimizing a kernel function and a penalty factor of the SVM by using a fusion particle swarm algorithm and an improved grey wolf algorithm, and constructing a PSO-IGWO-SVM model; and training and testing the PSO-IGWO-SVM model by using the test set, and outputting a fault diagnosis result of the analog circuit. By combining the advantages of the two algorithms, the classification effect and diagnosis precision of the SVM are improved. Meanwhile, by introducing a nonlinear convergence factor and a proportional weight dynamic adjustment strategy, the search efficiency and convergence speed of the algorithm are further improved.
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