摘要
为解决电熔镁炉工况识别模型泛化能力和可解释性弱的缺陷,提出一种基于深层卷积随机配置网络(Deep convolutional stochastic configuration networks,DCSCN)的可解释性电熔镁炉异常工况识别方法.首先,基于监督学习机制生成具有物理含义的高斯差分卷积核,采用增量式方法构建深层卷积神经网络(Deep convolutional neural network,DCNN),确保识别误差逐级收敛,避免反向传播算法迭代寻优卷积核参数的过程.定义通道特征图独立系数获取电熔镁炉特征类激活映射图的可视化结果,定义可解释性可信度评测指标,自适应调节深层卷积随机配置网络层级,对不可信样本进行再认知以获取最优工况识别结果.实验结果表明,所提方法较其他方法具有更优的识别精度和可解释性.
In order to solve the defects of generalization ability and weak interpretability of fused magnesium furnace working condition recognition model,an interpretable fused magnesium furnace abnormal working condition recognition method based on deep convolutional stochastic configuration networks(DCSCN)is proposed in this paper.Firstly,based on the supervised learning mechanism to generate Gaussian differential convolution kernel with physical meaning,an incremental method is used to construct a deep convolutional neural network(DCNN)to ensure that the recognition error converges step by step,and to avoid the process that back propagation algorithm iteratively finds the optimal convolutional kernel parameters.This paper defines channel feature map independent coefficients to obtain visualization results of fused magnesium furnace feature class activation mapping map,defines interpretable credibility measure to adaptively adjust deep convolutional stochastic configuration network layers,and recognizes untrustworthy samples to obtain optimal working condition recognition results.The experimental results show that the proposed method in this paper has better recognition accuracy and interpretability than other methods.
作者
李帷韬
童倩倩
王殿辉
吴高昌
LI Wei-Tao;TONG Qian-Qian;WANG Dian-Hui;WU Gao-Chang(School of Electrical Engineering and Automation,Hefei University of Technology,Hefei 230009;Institute of Artificial Intelligence,China University of Mining and Technology,Xuzhou 221116;State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenyang 110819)
出处
《自动化学报》
EI
CAS
CSCD
北大核心
2024年第3期527-543,共17页
Acta Automatica Sinica
基金
国家重点研发计划(2018AAA0100304)
国家自然科学基金(62173120,62103092)
安徽省自然科学基金(2108085UD11)
111引智项目(BP0719039)资助。
