# Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analytics

arXiv: https://arxiv.org/abs/2407.11038 · HTML: https://mlsanity.com/paper/2407.11038/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 63 references that are in the map.
- First posted on arXiv 6 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Stable Recurrent Models](https://mlsanity.com/paper/1805.10369/) (2018)
- [Stochastic Configuration Networks: Fundamentals and Algorithms](https://mlsanity.com/paper/1702.03180/) (2017)
- [Recurrent Stochastic Configuration Networks for Temporal Data Analytics](https://mlsanity.com/paper/2406.16959/) (2024)
- [A self-organizing fuzzy neural network for sequence learning](https://mlsanity.com/paper/1908.00617/) (2019)
- [Stochastic Configuration Machines for Industrial Artificial Intelligence](https://mlsanity.com/paper/2308.13570/) (2023)
- [Stochastic Configuration Networks Ensemble for Large-Scale Data Analytics](https://mlsanity.com/paper/1707.00300/) (2017)

## Built on by (most cited first, 4 of 4)

- [KANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning](https://mlsanity.com/paper/2602.03034/) (2026)
- [Deep Recurrent Stochastic Configuration Networks for Modelling Nonlinear Dynamic Systems](https://mlsanity.com/paper/2410.20904/) (2024)
- [Deeper Insights into Learning Performance of Stochastic Configuration Networks](https://mlsanity.com/paper/2411.08544/) (2024)
- [Kernel Stochastic Configuration Networks for Nonlinear Regression](https://mlsanity.com/paper/2412.05846/) (2024)

## Abstract (arXiv)

This paper presents a novel neuro-fuzzy model, termed fuzzy recurrent stochastic configuration networks (F-RSCNs), for industrial data analytics. Unlike the original recurrent stochastic configuration network (RSCN), the proposed F-RSCN is constructed by multiple sub-reservoirs, and each sub-reservoir is associated with a Takagi-Sugeno-Kang (TSK) fuzzy rule. Through this hybrid framework, first, the interpretability of the model is enhanced by incorporating fuzzy reasoning to embed the prior knowledge into the network. Then, the parameters of the neuro-fuzzy model are determined by the recurrent stochastic configuration (RSC) algorithm. This scheme not only ensures the universal approximation property and fast learning speed of the built model but also overcomes uncertain problems, such as unknown dynamic orders, arbitrary structure determination, and the sensitivity of learning parameters in modelling nonlinear dynamics. Finally, an online update of the output weights is performed using the projection algorithm, and the convergence analysis of the learning parameters is given. By integrating TSK fuzzy inference systems into RSCNs, F-RSCNs have strong fuzzy inference capability and can achieve sound performance for both learning and generalization. Comprehensive experiments show that the proposed F-RSCNs outperform other classical neuro-fuzzy and non-fuzzy models, demonstrating great potential for modelling complex industrial systems.