mlsanityData to 7 Oct 2026

Stochastic Configuration Networks: Fundamentals and Algorithms

arXiv 1702.03180 ↗First posted 10 Feb 2017cs.NEcited by 34

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Abstract (arXiv)

This paper contributes to a development of randomized methods for neural networks. The proposed learner model is generated incrementally by stochastic configuration (SC) algorithms, termed as Stochastic Configuration Networks (SCNs). In contrast to the existing randomised learning algorithms for single layer feed-forward neural networks (SLFNNs), we randomly assign the input weights and biases of the hidden nodes in the light of a supervisory mechanism, and the output weights are analytically evaluated in either constructive or selective manner. As fundamentals of SCN-based data modelling techniques, we establish some theoretical results on the universal approximation property. Three versions of SC algorithms are presented for regression problems (applicable for classification problems as well) in this work. Simulation results concerning both function approximation and real world data regression indicate some remarkable merits of our proposed SCNs in terms of less human intervention on the network size setting, the scope adaptation of random parameters, fast learning and sound generalization.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Feb 2017.

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Citing papers per year: 2021: 8; 2022: 4; 2023: 10; 2024: 7; 2025: 2; 2026: 3.

34 papers.

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  1. 2021Rethinking and Improving the Robustness of Image Style Transfercited by 48
  2. 2021Theory of Deep Convolutional Neural Networks III: Approximating Radial Functionscited by 22
  3. 2023Label-Efficient Learning in Agriculture: A Comprehensive Reviewcited by 16
  4. 2021A Modified Batch Intrinsic Plasticity Method for Pre-training the Random Coefficients of Extreme Learning Machinescited by 13
  5. 2021Demystification of Few-shot and One-shot Learningcited by 10
  6. 2024Recurrent Stochastic Configuration Networks for Temporal Data Analyticscited by 7
  7. 2022Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraintcited by 5
  8. 2023Stochastic Configuration Machines for Industrial Artificial Intelligencecited by 5
  9. 2021Cluster-based Input Weight Initialization for Echo State Networkscited by 4
  10. 2023Reliable Prediction Intervals with Directly Optimized Inductive Conformal Regression for Deep Learningcited by 4
  11. 2024Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learningcited by 4
  12. 2024Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analyticscited by 4
22 more
  1. 2026Enhancing Pretrained Model-based Continual Representation Learning via Guided Random Projectioncited by 4
  2. 2022Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network for Tabular Data Classificationcited by 3
  3. 2023Randomly Initialized Subnetworks with Iterative Weight Recyclingcited by 3
  4. 2023IF2Net: Innately Forgetting-Free Networks for Continual Learningcited by 3
  5. 2024Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networkscited by 2
  6. 2022Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analyticscited by 1
  7. 2023Stochastic Configuration Machines: FPGA Implementationcited by 1
  8. 2021Multi-Model Least Squares-Based Recomputation Framework for Large Data Analysiscited by 0
  9. 2021Autoencoder based Randomized Learning of Feedforward Neural Networks for Regressioncited by 0
  10. 2021Feature extraction and classification algorithm, which one is more essential? An experimental study on a specific task of vibration signal diagnosiscited by 0
  11. 2022A New Learning Paradigm for Stochastic Configuration Network: SCN+cited by 0
  12. 2023Complementary Learning Subnetworks for Parameter-Efficient Class-Incremental Learningcited by 0
  13. 2023Interpretable Neural Networks with Random Constructive Algorithmcited by 0
  14. 2023Cloud Ensemble Learning for Fault Diagnosis of Rolling Bearings with Stochastic Configuration Networkscited by 0
  15. 2023Constructive Incremental Learning for Fault Diagnosis of Rolling Bearings with Ensemble Domain Adaptationcited by 0
  16. 2024Self-Organizing Recurrent Stochastic Configuration Networks for Nonstationary Data Modellingcited by 0
  17. 2024Deep Recurrent Stochastic Configuration Networks for Modelling Nonlinear Dynamic Systemscited by 0
  18. 2024Effective Non-Random Extreme Learning Machinecited by 0
  19. 2025Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networkscited by 0
  20. 2025Broad stochastic configuration residual learning system for norm-convergent universal approximationcited by 0
  21. 2026Physics-Informed Broad Learning System: An Efficient Backpropagation-Free Framework for Solving Partial Differential Equationscited by 0
  22. 2026Physics-Informed Stochastic Configuration Machine: A Backpropagation-Free Neural Network with Fast Training for Nonlinear Differential Equationscited by 0