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Stochastic Configuration Machines for Industrial Artificial Intelligence

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arxiv 2308.13570 v6 pith:YJBRL7JK submitted 2023-08-25 cs.LG cs.AIcs.NE

Dianhui Wang , Matthew J. Felicetti This is my paper · ORCID
classification cs.LGcs.AIcs.NE
keywords industrialmodelconfigurationdatanetworksscmsstochasticapplications
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Real-time predictive modelling with desired accuracy is highly expected in industrial artificial intelligence (IAI), where neural networks play a key role. Neural networks in IAI require powerful, high-performance computing devices to operate a large number of floating point data. Based on stochastic configuration networks (SCNs), this paper proposes a new randomized learner model, termed stochastic configuration machines (SCMs), to stress effective modelling and data size saving that are useful and valuable for industrial applications. Compared to SCNs and random vector functional-link (RVFL) nets with binarized implementation, the model storage of SCMs can be significantly compressed while retaining favourable prediction performance. Besides the architecture of the SCM learner model and its learning algorithm, as an important part of this contribution, we also provide a theoretical basis on the learning capacity of SCMs by analysing the model's complexity. Experimental studies are carried out over some benchmark datasets and three industrial applications. The results demonstrate that SCM has great potential for dealing with industrial data analytics.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Kernel Stochastic Configuration Networks for Nonlinear Regression

    cs.LG 2024-12 conditional novelty 5.0 of 10

    KSCNs apply kernel ridge regression on top of supervised random SCN features and report improved regression accuracy and stability on three datasets.

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

    cs.CV 2025-01 reject novelty 3.0 of 10

    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 ...

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