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

arXiv: https://arxiv.org/abs/2501.02740 · HTML: https://mlsanity.com/paper/2501.02740/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 6 Jan 2025.

## Builds on (most cited first, 10 of 10)

- [Evaluating the visualization of what a Deep Neural Network has learned](https://mlsanity.com/paper/1509.06321/) (2015)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [Discrimination-aware Network Pruning for Deep Model Compression](https://mlsanity.com/paper/2001.01050/) (2020)
- [Stochastic Configuration Networks: Fundamentals and Algorithms](https://mlsanity.com/paper/1702.03180/) (2017)
- [BlockQNN: Efficient Block-wise Neural Network Architecture Generation](https://mlsanity.com/paper/1808.05584/) (2018)
- [1xN Pattern for Pruning Convolutional Neural Networks](https://mlsanity.com/paper/2105.14713/) (2021)
- [SlimConv: Reducing Channel Redundancy in Convolutional Neural Networks by Weights Flipping](https://mlsanity.com/paper/2003.07469/) (2020)
- [Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics](https://mlsanity.com/paper/2108.11939/) (2021)
- [Stochastic Configuration Machines for Industrial Artificial Intelligence](https://mlsanity.com/paper/2308.13570/) (2023)
- [A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture](https://mlsanity.com/paper/2207.04585/) (2022)

## Abstract (arXiv)

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.