摘要
由于浅层神经网络网络结构和训练方式的限制,网络学习能力和泛化能力在大样本条件下没有深度学习网络强,为此,提出了一种基于栈式混合编码器的水质传感器数据融合算法。该算法通过堆叠自动编码器和稀疏自动编码器形成深度学习网络模型,实现对样本数据的特征挖掘和稀疏表示。经过大规模样本训练后的网络模型能够拟合复杂非线性函数,对低质量的样本数据有一定的泛化能力,并提高预测分类的精度。仿真结果证明,提出的算法取得了更高的评价分类准确率。
Due to the limitation of network structure and training mode for shallow neural network, network learning ability and generalization ability are weaker than deep learning network under large sample conditions. Therefore, a data fusion algorithm of water quality sensor based on stacked hybrid encoder is proposed. The algorithm forms a deep learning network model by stacking automatic encoder and sparse automatic encoder, which realizes the feature mining and sparse representation of sample data. The network model can fit the complex nonlinear function after large-scale sample training, and has generalization ability for low quality sample data. It can improve the accuracy of prediction classification. Simulation results show that the proposed algorithm achieves higher classification accuracy.
作者
王照丽
杨一伟
黄凤辰
肖坚
徐立中
Wang Zhaoli;Yang Yiwei;Huang Fengchen;Xiao Jian;Xu Lizhong(Chengdu Academy of Environmental Sciences,Chengdu 610072,China;School of Computer and Information,Hohai University,Nanjing 211100,China)
出处
《电子测量技术》
北大核心
2021年第2期87-92,共6页
Electronic Measurement Technology
基金
广东省水利科技创新项目(2020-04)资助。
关键词
水质传感器
数据融合
栈式混合编码器
非线性函数
评价分类
water quality sensor
data fusion
stacked hybrid encoder
nonlinear function
evaluation classification
作者简介
王照丽,高级工程师,主要研究方向为数据融合处理、模式识别。E-mail:15150681569@163.com;杨一伟,硕士研究生,主要研究方向为电子信号处理、信道编码。E-mail:1097023730@qq.com;黄凤辰,副教授,主要研究方向为嵌入式控制、机器学习。E-mail:1103674925@qq.com。
