| 梅恒荣,殷礼胜,刘冬梅,何怡刚,袁莉芬,赵丽欣,陈鹏,赵蓓蕾,任帅.改进粒子群算法优化的SVM模拟电路故障诊断[J].电子测量与仪器学报,2017,31(8):1239-1246 |
| 改进粒子群算法优化的SVM模拟电路故障诊断 |
| Analogue circuit fault diagnosis based on SVM optimized by IPSO |
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| DOI：10.13382/j.jemi.2017.08.012 |
| 中文关键词: 支持向量机 改进粒子群算法 模拟电路 故障诊断 |
| 英文关键词:support vector machine improved particle swarm optimization analog circuit fault diagnosis |
| 基金项目:国家自然科学基金（51577046,11105037）、国家自然科学基金重点项目（51637004）、安徽省科技计划重点项目（1301022036）、国际热核聚变实验堆（ITER）计划专项(2015GB102000)、教育部科学技术研究重大项目（313018）资助 |
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| Author | Institution |
| Mei Hengrong | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
| Yin Lisheng | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
| Liu Dongmei | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
| He Yigang | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
| Yuan Lifen | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
| Zhao Lixin | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
| Chen Peng | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
| Zhao Beilei | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
| Ren Shuai | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
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| 中文摘要: |
| 针对粒子群（PSO）算法优化支持向量机（SVM）参数存在容易陷入局部最优的问题，通过引入新的动态惯性权重、全局邻域搜索、收缩因子和遗传算法中的变异操作，提出了一种基于改进粒子群（IPSO）算法优化SVM参数（IPSO SVM）的改进型分类器。采用UCI机器学习库中的公共数据集Iris、Wine和seeds来测试其分类效果，结果表明IPSO SVM分类器在分类准确率和分类时间上优于GS SVM、AFSA SVM、GA SVM和PSO SVM分类器。最后，将IPSO SVM分类器应用于Sallen Key带通滤波器、四运放双二次高通滤波器及非线性整流电路的故障诊断中，结果表明IPSO SVM分类器具有较强的全局收敛能力和较快的收敛速度。 |
| 英文摘要: |
| In order to solve the problem that the basic particle swarm (PSO) to optimize the parameter of SVM is easy to fall into local optimum, this paper proposes a modified classifier that uses the improved particle swarm optimization (IPSO) to optimize the parameter of SVM (IPSO SVM) by introducing the new dynamic inertia weight, global neighborhood search, shrinkage factor and mutation operator of genetic algorithm. The classification result is tested by the common datasets named Iris, Wine and seeds from UCI machine learning repository, the result shows that IPSO SVM classifier is better than GS SVM, AFSA SVM, GA SVM and PSO SVM classifier in terms of classification accuracy and classification time. The better convergence ability and speed of the IPSO SVM classifier are verified by fault diagnosis of Sallen Key band pass filter, four opamp biquad highpass filter and nonlinear rectifier circuit. |
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