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Lightweight convolutional neural network based on overlapping kernel compression pruning for modeling nonlinear systems

delete2026-09-09
delete0
PRE
AI
H
Honggui Han *
Y
Yuan Wang
Z
Zecheng Tang
X
Xiaolong Wu
DOI：10.1007/s11431-026-3359-9delete
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摘要

摘要

En 中文
卷积神经网络（CNNs）通过堆叠大量卷积核，利用空间特征过滤来建模非线性系统。然而，具有相似空间权重排列的重叠核因重复特征提取引入虚假相关性，导致高计算成本和有限的泛化能力。为解决此问题，本文提出了一种基于重叠核压缩剪枝的轻量化卷积神经网络（LCNN）。首先，设计了一种重叠核感知（OKP）方法来评估LCNN的核冗余度。OKP方法采用谱聚类将相似核分组到图拉普拉斯谱空间中，并通过纳入具有相似空间权重排列的重叠核来扩大冗余范围。其次，构建了一种核压缩剪枝（KCP）策略来剪除冗余核。在核冗余的指导下，KCP策略对每组相似核执行奇异值分解，并自适应截断分解矩阵的秩以重建紧凑的代表性核集合。第三，开发了一种正则化梯度下降算法来剪枝后微调LCNN的参数。最后，在三个工业数据集上的实验结果证实了LCNN相比其他模型的优越泛化性能。
Keyword:
convolutional neural networks
kernel compression pruning
generalization
kernel redundancy

期刊

Science China-Technological Sciences 封面图
Science China-Technological Sciences
IF:
4.9
论文数:
5.0K
被引数:
9.9K

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