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一种使用改进的子搜索算法和支持矢量机器的模拟电路故障诊断方法
Guohua Wang1, Yiwei Tu1, Jing Nie1
1School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
本研究介绍了一种改进的Sparrow搜索算法 (ISSA),用于增强模拟电路故障诊断. 与标准方法相比,ISSA-SVM模型显著提高了诊断准确度.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 电路分析 电路分析
背景情况:
- 模拟电路故障诊断面临由于组件公差和非线性而面临的挑战.
- 现有的方法在识别软故障时可能缺乏准确性和稳定性.
研究的目的:
- 为模拟电路开发一个优化的软故障诊断方法.
- 为了提高Sparrow搜索算法 (SSA) 的性能,以改进支持矢量机 (SVM) 参数优化.
主要方法:
- 通过通过四种优化策略解决SSA缺陷,开发了一种改进的Sparrow搜索算法 (ISSA).
- ISSA与使用23个函数的其他群集智能算法进行了基准测试,证明了卓越的融合速度,准确性和稳定性.
- 优化的ISSA被用来调整SVM参数,创建ISSA-SVM故障诊断模型.
主要成果:
- 在优化实验中,ISSA表现出更快的融合,更高的准确性和更好的稳定性.
- 在Sallen-key测试电路实验中,ISSA-SVM模型实现了98.15%的正确故障诊断率.
- 这比标准的SSA-SVM模型有所改善,该模型的诊断率为97.41%.
结论:
- 优化的ISSA-SVM模型为模拟电路软故障诊断提供了有效的方法.
- 拟议的方法显示了更高的诊断准确性和稳定性.
- 这项研究有助于在模拟电子系统中更可靠地检测故障.
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