A stochastic configuration CNN with reinforcement-learning kernel pruning classifies four fused magnesium furnace working conditions at 92.57% accuracy, but the proof of convergence and the interpretability advantage are not well supported.
Signal-compensation-based adaptive PID control for fused magnesia smelting processes,
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Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks
A stochastic configuration CNN with reinforcement-learning kernel pruning classifies four fused magnesium furnace working conditions at 92.57% accuracy, but the proof of convergence and the interpretability advantage are not well supported.