Analogue Circuit Fault Diagnosis Based on SVM Optimized by IGSA

MEI Heng-rong; LIU Dong-mei; HE Yi-gang; YIN Li-sheng; ZHAO Li-xin; ZHAO Bei-lei

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Analogue Circuit Fault Diagnosis Based on SVM Optimized by IGSA

更新时间：2026-09-04

- Analogue Circuit Fault Diagnosis Based on SVM Optimized by IGSA
- Vol. 35, Issue 5, Pages: 109-115(2018)
- 作者机构：
  
  合肥工业大学电气与自动化工程学院,安徽,合肥,230009
- 作者简介：
- 基金信息：
- DOI：
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- Published：2018
- 稿件说明：
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梅恒荣, 刘冬梅, 何怡刚, 殷礼胜, 赵丽欣, 赵蓓蕾. 改进引力搜索算法优化的SVM模拟电路故障诊断[J]. 微电子学与计算机, 2018, 35(5): 109-115.

MEI Heng-rong, LIU Dong-mei, HE Yi-gang, et al. Analogue Circuit Fault Diagnosis Based on SVM Optimized by IGSA[J]. 2018, 35(5): 109-115.
梅恒荣, 刘冬梅, 何怡刚, 殷礼胜, 赵丽欣, 赵蓓蕾. 改进引力搜索算法优化的SVM模拟电路故障诊断[J]. 微电子学与计算机, 2018, 35(5): 109-115. DOI：

MEI Heng-rong, LIU Dong-mei, HE Yi-gang, et al. Analogue Circuit Fault Diagnosis Based on SVM Optimized by IGSA[J]. 2018, 35(5): 109-115. DOI：

摘要

文本在引力搜索算法(GSA) 的基础上

通过引入粒子群算法中的惯性权重和全局记忆性、时变引力搜索策略和边界变异策略

提出一种改进引力搜索算法(IGSA) 来优化SVM参数(IGSA-SVM) 的改进型分类器.首先选取三个UCI数据集进行仿真分析

结果表明IGSA-SVM分类器在分类准确率和分类时间上优于GS-SVM、GASVM、PSO-SVM和GSA-SVM分类器.然后分别采用线性和非线性模拟电路来进行故障诊断

结果表明IGSASVM分类器能有效地防止局部收敛并提高了诊断的优化效率.

Abstract

This paper proposes a modified classifier that uses the improved gravity search algorithm (IGSA) to optimize the parameter of SVM (IGSA-SVM) by introducing the inertia weight and global memory in particle swarm algorithm

time-varying gravitational search strategy and boundary mutation strategy.At first

three UCI datasets are selected for simulation analysis

and the results show that IGSA-SVM classifier is better than GS-SVM

GASVM

PSO-SVM and GSA-SVM classifiers in classification accuracy and classification time.Then the linear and nonlinear analog circuits are used as fault diagnosis circuits respectively

and the results show that the IGSA-SVM classifier can effectively prevent local convergence and improve the efficiency of diagnosis.

关键词

Keywords

references

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许军, 张耀辉, 陈圣俭, 等. 基于结点电压增量的模拟电路多故障诊断方法研究[J]. 微电子学与计算机, 2015, 32 (2): 138-141. DOI: 138

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