# Stochastic Configuration Machines for Industrial Artificial Intelligence

arXiv: https://arxiv.org/abs/2308.13570 · HTML: https://mlsanity.com/paper/2308.13570/

## 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 (most cited first, 15 of 15)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [Deep Learning with Limited Numerical Precision](https://mlsanity.com/paper/1502.02551/) (2015)
- [Quantized Convolutional Neural Networks for Mobile Devices](https://mlsanity.com/paper/1512.06473/) (2015)
- [Ternary Weight Networks](https://mlsanity.com/paper/1605.04711/) (2016)
- [Regularizing Activation Distribution for Training Binarized Deep Networks](https://mlsanity.com/paper/1904.02823/) (2019)
- [Stochastic Configuration Networks: Fundamentals and Algorithms](https://mlsanity.com/paper/1702.03180/) (2017)
- [Ternary Neural Networks for Resource-Efficient AI Applications](https://mlsanity.com/paper/1609.00222/) (2016)
- [CatBoost: unbiased boosting with categorical features](https://mlsanity.com/paper/1706.09516/) (2017)
- [Bitwise Neural Networks](https://mlsanity.com/paper/1601.06071/) (2016)
- [A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes](https://mlsanity.com/paper/1710.04874/) (2017)
- [Deep Stochastic Configuration Networks with Universal Approximation Property](https://mlsanity.com/paper/1702.05639/) (2017)

## Built on by (most cited first, 5 of 5)

- [Recurrent Stochastic Configuration Networks for Temporal Data Analytics](https://mlsanity.com/paper/2406.16959/) (2024)
- [Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analytics](https://mlsanity.com/paper/2407.11038/) (2024)
- [Stochastic Configuration Machines: FPGA Implementation](https://mlsanity.com/paper/2310.19225/) (2023)
- [Kernel Stochastic Configuration Networks for Nonlinear Regression](https://mlsanity.com/paper/2412.05846/) (2024)
- [Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks](https://mlsanity.com/paper/2501.02740/) (2025)

## 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.