Optimization of analog circuit fault diagnosis parameters based on SVM and genetic algorithm

被引：0

作者：

机构：

[1] Department of Control Engineering, NAEI

来源：

Jing, T. | 1600年 / Advanced Institute of Convergence Information Technology卷 / 04期

关键词：

Analog circuit; Fault diagnosis; Genetic algorithm; Kernel function; Parameters optimization; Support vector machine;

D O I：

10.4156/AISS.vol4.issue4.6

中图分类号：

学科分类号：

摘要：

Currently, Diagnostic parameters of analog circuits fault diagnosis based on SVM are adjusted in accordance with the principle to determine the global optimum or by trial. Parameter adjustment is not considered practical diagnostic system diagnostic requirements. It can not be part of various diagnostic parameters simultaneously adjust and optimize. The results are not satisfactory. The paper presents a model of fitness function for genetic algorithm parameter optimization, It will convert the actual circuit diagnosis requires fitness indicators in the evaluation results of analog circuit fault diagnosis; In this paper, a circuit diagnosis framework for closed-loop model parameters optimization based on genetic algorithm is presented. It's all part of the system parameters to optimize simultaneously, and analyzes the convergence of the algorithm parameter search. By example the closed-loop fault diagnosis diagnosis parameter optimization framework developed under the parameters of the various parts of the impact on decision-making. This article describes the establishment of closed-loop fault diagnosis model parameter optimization framework and the search algorithm is effective and practical.

引用

页码：42 / 50

页数：8

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