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| 基于径向基函数神经网络的模拟/混合电路故障诊断 | |
| 引用本文： | 王承,陈光(礻禹),谢永乐. 基于径向基函数神经网络的模拟/混合电路故障诊断[J]. 电路与系统学报, 2007, 12(2): 65-68 |
| 作者姓名： | 王承 陈光(礻禹) 谢永乐 |
| 作者单位： | 1. 电子科技大学,自动化工程学院CAT研究室,四川,成都,610054;ZTE中兴通讯股份有限公司,康讯研究所,广东,深圳,518057 2. 电子科技大学,自动化工程学院CAT研究室,四川,成都,610054 |
| 基金项目： | 国家自然科学基金，四川省青年科技基金 |
| 摘 要： | 径向基函数神经网络是一种前馈型神经网络,具有较强的函数逼近能力和分类能力,学习速度快等优点.本文采用幅值恒定的正弦信号源进行模拟电路的故障仿真,从频域提取输出信号波形的特征值建立故障字典,应用径向基函数神经网络的这些优点进行响应分析和故障诊断,能够实现快速故障诊断及定位,具有准确率高的特点. |
| 关 键 词： | 径向基函数 混合集成电路 故障诊断 神经网络 |
| 文章编号： | 1007-0249（2007）02-0065-04 |
| 收稿时间： | 2003-08-29 |
| 修稿时间： | 2003-11-25 |
Fault diagnosis based on radial basis function neural network in analog and mixed-signal circuits |
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| WANG Cheng,CHEN Guang-ju,XIE Yong-le. Fault diagnosis based on radial basis function neural network in analog and mixed-signal circuits[J]. Journal of Circuits and Systems, 2007, 12(2): 65-68 | |
| Authors: | WANG Cheng CHEN Guang-ju XIE Yong-le |
| Abstract: | Radial basis function neural network is a feed-forward network. It has many good properties, such as powerful ability for function approximation, classification and learning rapidly. Sinusoidal input to the analog circuit with constant amplitude and different frequencies is simulated and frequency domain features of the output response are used to build the fault dictionary. In this paper, a radial basis function neural network method for response analysis and fault diagnosis has been proposed. Results illustrate that this method is feasible and has many powerful features, such as diagnosing and locating faults quickly and exactly. |
| Keywords: | radial basis function mixed-signal integrated circuits fault diagnosis neural network |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |