| 姜月明,俞 洋,彭喜元.基于关键特征提取和 Elman 神经网络的
开关电源多参数辨识[J].电子测量与仪器学报,2021,35(7):11-19 |
| 基于关键特征提取和 Elman 神经网络的
开关电源多参数辨识 |
| Multi-parameter identification of switch mode power supplybased on key features and elman neural network |
| |
| DOI： |
| 中文关键词: 开关电源 关键特征 Elman 神经网络 多参数辨识 |
| 英文关键词:switch mode power supply(SMPS) key features elman neural network multi-parameter identification |
| 基金项目:国家自然科学基金（61571161，62071150）项目资助 |
|
|
|
|
| 摘要点击次数: 436 |
| 全文下载次数: 541 |
| 中文摘要: |
| 开关电源作为电子系统的重要组成部件,一旦发生故障将会对整个电子系统带来不可估量的损失,所以需要对开关电
源的元器件参数进行及时准确地辨识,以便有效地评估开关电源的健康状态。 受环境应力的影响,在实际工作中开关电源的多
个元器件参数均会发生退化。 为有效地辨识开关电源的状态,提出基于关键特征和 Elman 神经网络的开关电源多参数辨识方
法,方法首先利用小波包分析提取局部能量特征;为提高辨识精度,将变异系数作为优选局部能量特征的标准,提取具有较大变
异系数的局部能量特征作为关键特征;最后,采用 Elman 神经网络建立关键特征与辨识参数的关联。 仿真实验和硬件实验结果
证明具有较高的辨识精度和良好的实用性。 |
| 英文摘要: |
| Switch mode power supply (SMPS) is an important component of the electronic system, the fault state of SMPS has an adverse
impact on the operation of the back-end components and the entire electronic system. Therefore, it is very necessary to identify the health
state of SMPS. Under the environmental stresses, Multi-parameters of the components of SMPS will degrade. To effectively identify the
state of SMPS, the paper presents the multi-parameter identification method based on the key features and Elman neural network. At
first, the paper obtains the Wavelet Packet local energy features of the output. To improve the identification accuracy, the coefficient of
variation are used to select the local energy features, the local energy features with lager coefficient of variation values were regarded as
the key features. Finally, the relationship between the key features and parameters will be established based on Elman neural network.
The results of the simulation and hardware experiments demonstrate that the proposed method can obtain the high identification accuracy
and great practicability. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
