关键词: 核函数, 遗传算法, 模拟电路, 故障诊断, 支持向量机, 参数优化

Abstract: 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 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.

Key words: kernel function, genetic algorithm, analog circuit, fault diagnosis, support vector machine, parameters optimization