A Parameter Optimization Method of Finite Exhaustion-Local GA for SVDD and Fault Diagnosis Application

CAI Jin-yan; DU Min-jie; MENG Ya-feng; ZHU Sai

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A Parameter Optimization Method of Finite Exhaustion-Local GA for SVDD and Fault Diagnosis Application

更新时间：2026-09-04

- A Parameter Optimization Method of Finite Exhaustion-Local GA for SVDD and Fault Diagnosis Application
- Vol. 30, Issue 2, Pages: 13-17(2013)
- 作者机构：
  
  军械工程学院光学与电子工程系,河北,石家庄,050003
- 作者简介：
- 基金信息：
- DOI：
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- Published：2013
- 稿件说明：
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蔡金燕, 杜敏杰, 孟亚峰, 朱赛. SVDD参数优化的有限穷举—局部遗传算法及故障检测应用[J]. 微电子学与计算机, 2013, 30(2): 13-17.

CAI Jin-yan, DU Min-jie, MENG Ya-feng, et al. A Parameter Optimization Method of Finite Exhaustion-Local GA for SVDD and Fault Diagnosis Application[J]. 2013, 30(2): 13-17.
蔡金燕, 杜敏杰, 孟亚峰, 朱赛. SVDD参数优化的有限穷举—局部遗传算法及故障检测应用[J]. 微电子学与计算机, 2013, 30(2): 13-17. DOI：

CAI Jin-yan, DU Min-jie, MENG Ya-feng, et al. A Parameter Optimization Method of Finite Exhaustion-Local GA for SVDD and Fault Diagnosis Application[J]. 2013, 30(2): 13-17. DOI：

摘要

针对支持向量数据描述(SVDD)训练过程中的参数优化问题

提出了一种有限穷举—局部遗传算法.首先

在分别分析参数C和σ对SVDD分类性能不同影响的基础上

得到参数σ是影响分类性能主因的结论.然后针对σ的优化问题

通过穷举有限个整数解并比较其分类性能来确定近似最优解

在近似最优解的领域内用遗传算法进行局部搜索

最终得到精确的优化参数.仿真实验及电路故障检测应用结果表明:算法有效避免了参数搜索的盲目性

能以更短的时耗逼近最优解.

Abstract

Aiming at the problem of SVDD parameter optimization in training process

an finite exhaustion-local genetic algorithm is proposed.Based on the analysis of C and σ parameters different influence on SVDD classfication performance respectively

a conclusion that σ is the main influencing factor is made.Focus on σ optimization issue

the near-optimum solution of σ is determined by enumerating finite integer solutions along with comparing these solutions performance

and then search is launched locally in nearby domain of near-optimum solution.The precise parameter is get at last.Simulation experiment and circuit fault detection application results show that the above algorithm avoids blind parameter searching and can approach optimum solution in shorter time wastes.

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