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.
维普
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