mlsanityData to 14 Oct 2026

Stochastic Configuration Machines for Industrial Artificial Intelligence

arXiv 2308.13570 ↗First posted 25 Aug 2023cs.LG cs.AI cs.NEcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 15 of its 58 references that are in the map. First posted on arXiv 25 Aug 2023.

Builds on

15 of 58 references.

References · 15

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3 more
  1. 2016Bitwise Neural Networkscited by 29
  2. 2017A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodescited by 9
  3. 2017Deep Stochastic Configuration Networks with Universal Approximation Propertycited by 2

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Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 1.

5 papers.

  1. 2024Recurrent Stochastic Configuration Networks for Temporal Data Analyticscited by 7
  2. 2024Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analyticscited by 4
  3. 2023Stochastic Configuration Machines: FPGA Implementationcited by 1
  4. 2024Kernel Stochastic Configuration Networks for Nonlinear Regressioncited by 0
  5. 2025Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networkscited by 0