Dianci bileiqi, 2014 谐波的检测分析对于电力系统谐波治理非常重要,但常用的FFT检测方法存在频谱泄漏及栅栏现象等缺陷,加窗插值算法的使用一定程度上弥补了这些不足却又增大了计算量和存储容量要求;人工神经网络具有快速处理数字信号能力,本文以谐波离散傅里叶变换后的三角函数和傅里叶系数分别作为BP网络的隐层神经元和权值可获得了一种训练速度更快的神经网络;通过该神经网络算法和效果相对较好的几种FFT插值算法的仿真实例比较,验证了该算法能够更快更精确地对电力系统谐波进行分析,对谐波治理具有较大意义。
李德超
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[The current applicating state of neural network-based electroencephalogram diagnosis of Alzheimer's disease]. [PDF]
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, 2022 Liu Y +7 more
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[A bio-inspired hierarchical spiking neural network with biological synaptic plasticity for event camera object recognition]. [PDF]
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, 2023 Zhou Q, Zheng P, Li X.
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[Research on fatigue recognition based on graph convolutional neural network and electroencephalogram signals]. [PDF]
Sheng Wu Yi Xue Gong Cheng Xue Za ZhiLi S, Fu Y, Zhang Y, Lu G.
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[Diagnosis of nasopharyngeal carcinoma with convolutional neural network on narrowband imaging]. [PDF]
Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi, 2023 Weng J +9 more
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[Application of an interpretable neural network framework based on the LASSO-proj algorithm for warfarin dose prediction]. [PDF]
Sheng Wu Yi Xue Gong Cheng Xue Za ZhiZhong C, Zhu Y, Gu X.
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[Research on mild cognitive impairment diagnosis based on Bayesian optimized long-short-term neural network model]. [PDF]
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, 2023 Li X +6 more
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Zhongshan Daxue xuebao. Yixue kexue ban, 2008 【目的】微钙化点是早期乳腺癌的重要征象之一，本研究联合运用遗传算法、模糊数学和人工神经网络，建议一种乳腺微钙化点提取的新方法，为乳腺病变的自动识别提供前期处理，为早期乳腺癌的临床诊断提供帮助。【方法】首先利用随机方法产生大量的样本，然后，利用模糊遗传算法对产生的随机样本进行分类，将分类后的样本输入人工神经网络进行训练，将310幅乳腺图像的感兴趣区域输入训练后的人工神经网络分类器进行分类。【结果】与微钙化点提取方面的同类文献相比较，结果表明该算法在相同误检率下得到较高的阳性检出率。【结论 ...
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[Coronary artery segmentation based on Transformer and convolutional neural networks dual parallel branch encoder neural network]. [PDF]
Sheng Wu Yi Xue Gong Cheng Xue Za ZhiPan D, Luo G, Zeng A.
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