Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks

TL;DR

This paper introduces an interpretable deep learning model using stochastic configuration networks for recognizing working conditions in fused magnesium furnaces, improving accuracy and interpretability without relying on traditional backpropagation.

Contribution

It proposes a novel DCSCN-based method with reinforcement learning-based kernel pruning, enhancing interpretability and performance in industrial furnace condition recognition.

Findings

Outperforms other deep learning methods in accuracy

Provides visual feature activation maps for interpretability

Achieves a compact and highly accurate recognition model

Abstract

To address the issues of a weak generalization capability and interpretability in working condition recognition model of a fused magnesium furnace, this paper proposes an interpretable working condition recognition method based on deep convolutional stochastic configuration networks (DCSCNs). Firstly, a supervised learning mechanism is employed to generate physically meaningful Gaussian differential convolution kernels. An incremental method is utilized to construct a DCSCNs model, ensuring the convergence of recognition errors in a hierarchical manner and avoiding the iterative optimization process of convolutional kernel parameters using the widely used backpropagation algorithm. The independent coefficient of channel feature maps is defined to obtain the visualization results of feature class activation maps for the fused magnesium furnace. A joint reward function is constructed based on the recognition accuracy, the interpretable trustworthiness evaluation metrics, and the model parameter quantity. Reinforcement learning (RL) is applied to adaptively prune the convolutional kernels of the DCSCNs model, aiming to build a compact, highly performed and interpretable network. The experimental results demonstrate that the proposed method outperforms the other deep learning approaches in terms of recognition accuracy and interpretability.