首页|基于深层卷积随机配置网络的电熔镁炉工况识别方法研究

基于深层卷积随机配置网络的电熔镁炉工况识别方法研究

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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 con-volutional 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.
Research on Fused Magnesium Furnace Working Condition Recognition Method Based on Deep Convolutional Stochastic Configuration Networks
In order to solve the defects of generalization ability and weak interpret ability of fused magnesium fur-nace 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 pa-per.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 en-sure that the recognition error converges step by step,and to avoid the process that back propagation algorithm it-eratively 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.

Fused magnesium furnacedeep convolutional stochastic configuration networks(DCSCN)Gaussian differential convolution kernelclass activation mapping mapinterpretability

李帷韬、童倩倩、王殿辉、吴高昌

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合肥工业大学电气与自动化工程学院 合肥 230009

中国矿业大学人工智能研究院 徐州 221116

东北大学流程工业综合自动化国家重点实验室 沈阳 110819

电熔镁炉 深层卷积随机配置网络 高斯差分卷积核 类激活映射图 可解释性

国家重点研发计划国家自然科学基金国家自然科学基金安徽省自然科学基金高等学校学科创新引智计划(111计划)

2018AAA010030462173120621030922108085UD11BP0719039

2024

10.16383/j.aas.c230272

自动化学报
中国自动化学会 中国科学院自动化研究所

自动化学报

CSTPCD北大核心
影响因子：1.762
ISSN：0254-4156
年,卷(期)：2024.50(3)
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