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Reinforced node selection-based broad learning system and its applications
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DOI：10.1016/j.engappai.2026.113735.png)
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Abstract
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• The RNS-BLS model uses deep reinforcement learning to optimize the Broad Learning System by pruning redundant nodes. • A group-relevance scanning strategy uses the maximum information coefficient to reorder nodes, enhancing policy network optimization. • A sparse state representation and an STS fuzzy approximator stabilize the modeling process.
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