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本文提出基于Volterra核和模拟退火遗传混合算法的模拟电路故障诊断激励优化方法。在以Volterra核为特征向量的模拟电路故障诊断中,以相同激励信号下电路各故障状态的特征向量的集总欧氏距离作为适应度函数,对用于激励的多频正弦信号的参数进行优化,首先利用模拟退火算法形成精英团队,然后利用遗传算法寻找最佳激励信号的参数,从而提高故障诊断的效率。文中给出了退火遗传混合算法的优化方案和流程,并通过实例加以验证。
In this paper, a novel fault diagnosis and excitation optimization method for analog circuits based on hybrid algorithm of Volterra kernel and simulated annealing genetic algorithm is proposed. In the analog circuit fault diagnosis with Volterra kernel as eigenvector, the parameters of the multi-frequency sinusoidal signal for excitation are optimized by using the lumped Euclidean distance of eigenvectors of each fault state under the same excitation signal as the fitness function , The first use of simulated annealing algorithm to form elite team, and then use genetic algorithm to find the best excitation signal parameters, thereby improving fault diagnosis efficiency. In this paper, the optimal scheme and flow of annealing genetic hybrid algorithm are given and verified by examples.