# Stochastic Configuration Networks: Fundamentals and Algorithms

arXiv: https://arxiv.org/abs/1702.03180 · HTML: https://mlsanity.com/paper/1702.03180/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2017.

## Built on by (most cited first, 30 of 34)

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- [Demystification of Few-shot and One-shot Learning](https://mlsanity.com/paper/2104.12174/) (2021)
- [Recurrent Stochastic Configuration Networks for Temporal Data Analytics](https://mlsanity.com/paper/2406.16959/) (2024)
- [Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint](https://mlsanity.com/paper/2212.09062/) (2022)
- [Reliable Prediction Intervals with Directly Optimized Inductive Conformal Regression for Deep Learning](https://mlsanity.com/paper/2302.00872/) (2023)
- [Stochastic Configuration Machines for Industrial Artificial Intelligence](https://mlsanity.com/paper/2308.13570/) (2023)
- [Cluster-based Input Weight Initialization for Echo State Networks](https://mlsanity.com/paper/2103.04710/) (2021)
- [Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning](https://mlsanity.com/paper/2406.02428/) (2024)
- [Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analytics](https://mlsanity.com/paper/2407.11038/) (2024)
- [Enhancing Pretrained Model-based Continual Representation Learning via Guided Random Projection](https://mlsanity.com/paper/2603.19145/) (2026)
- [Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network for Tabular Data Classification](https://mlsanity.com/paper/2201.05809/) (2022)
- [Randomly Initialized Subnetworks with Iterative Weight Recycling](https://mlsanity.com/paper/2303.15953/) (2023)
- [IF2Net: Innately Forgetting-Free Networks for Continual Learning](https://mlsanity.com/paper/2306.10480/) (2023)
- [Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networks](https://mlsanity.com/paper/2404.03992/) (2024)
- [Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics](https://mlsanity.com/paper/2205.13191/) (2022)
- [Stochastic Configuration Machines: FPGA Implementation](https://mlsanity.com/paper/2310.19225/) (2023)
- [Multi-Model Least Squares-Based Recomputation Framework for Large Data Analysis](https://mlsanity.com/paper/2101.01271/) (2021)
- [Autoencoder based Randomized Learning of Feedforward Neural Networks for Regression](https://mlsanity.com/paper/2107.01711/) (2021)
- [Feature extraction and classification algorithm, which one is more essential? An experimental study on a specific task of vibration signal diagnosis](https://mlsanity.com/paper/2112.09389/) (2021)
- [A New Learning Paradigm for Stochastic Configuration Network: SCN+](https://mlsanity.com/paper/2203.07068/) (2022)
- [Complementary Learning Subnetworks for Parameter-Efficient Class-Incremental Learning](https://mlsanity.com/paper/2306.11967/) (2023)
- [Interpretable Neural Networks with Random Constructive Algorithm](https://mlsanity.com/paper/2307.00185/) (2023)
- [Cloud Ensemble Learning for Fault Diagnosis of Rolling Bearings with Stochastic Configuration Networks](https://mlsanity.com/paper/2307.00507/) (2023)
- [Constructive Incremental Learning for Fault Diagnosis of Rolling Bearings with Ensemble Domain Adaptation](https://mlsanity.com/paper/2308.14983/) (2023)
- [Self-Organizing Recurrent Stochastic Configuration Networks for Nonstationary Data Modelling](https://mlsanity.com/paper/2410.10072/) (2024)
- [Deep Recurrent Stochastic Configuration Networks for Modelling Nonlinear Dynamic Systems](https://mlsanity.com/paper/2410.20904/) (2024)
- [Effective Non-Random Extreme Learning Machine](https://mlsanity.com/paper/2411.16229/) (2024)

## 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.