基于Sailfish算法优化SKELM的模拟电路故障诊断

IF 4.6 Q1 OPTICS Journal of Physics-Photonics Pub Date : 2023-11-01 DOI:10.1088/1742-6596/2632/1/012014
Ming Ding, Runping Ma
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引用次数: 0

摘要

摘要针对叠核极限学习机(SKELM)网络模型参数优化训练时间长、模型泛化能力不确定的问题,提出了一种基于旗鱼算法的模拟电路故障诊断模型来优化叠核极限学习机(SKELM)。该模型引入核函数构建多层KELM,提高了前馈神经网络的泛化能力和学习速度。通过基于KELM的自动编码器训练,得到SKELM各层的权值。由于KELM-AE不需要设置初始参数,提高了训练速度。但是KELM-AE的核参数和正则化系数都是手动设置的,因此采用旗鱼优化器(sailfish optimizer, SFO)对这两个参数进行优化,然后通过逐层训练建立最优的SKELM模型。最后,采用Leap frog滤波电路作为仿真实验电路,并与优化后的SELM进行了比较。结果表明,KELM-AE具有较强的泛化能力,无需单独提取故障特征,即可通过非线性映射将故障特征映射到高维特征空间,从而提高了分类精度。
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Fault Diagnosis of Analog Circuits Based on The Sailfish Algorithm Optimized SKELM
Abstract In view of the long training time for the optimization of the network model parameters of the SELM and the uncertainty of the model generalization ability, this paper proposes an analog circuit fault diagnosis model based on the sailfish algorithm to optimize the stacked kernel extreme learning machine(SKELM). This model introduces a kernel function to build a multi-layer KELM, which can improve the generalization ability and learning speed of the feedforward neural network. The weights of each layer of SKELM are obtained through the automatic encoder training based on the KELM. Since KELM-AE does not need to set initial parameters, the training speed is improved. However, the kernel parameters and regularization coefficients of KELM-AE are set manually, so the sailfish optimizer (SFO) is used to optimize these two parameters, and then the optimal SKELM model is built through layer by layer training. Finally, the Leap frog filter circuit is used as the simulation experiment circuit, and further compared with the optimized SELM. The results show that KELM-AE has strong generalization ability, and it can map fault features to high-dimensional feature space through nonlinear mapping without extracting fault features separately, thus improving the classification accuracy.
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来源期刊
CiteScore
10.70
自引率
0.00%
发文量
27
审稿时长
12 weeks
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