# Recurrent Stochastic Configuration Networks for Temporal Data Analytics

arXiv: https://arxiv.org/abs/2406.16959 · HTML: https://mlsanity.com/paper/2406.16959/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 54 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021)
- [Stochastic Configuration Networks: Fundamentals and Algorithms](https://mlsanity.com/paper/1702.03180/) (2017)
- [Recurrent Neural Networks and Long Short-Term Memory Networks: Tutorial and Survey](https://mlsanity.com/paper/2304.11461/) (2023)
- [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)
- [Mod-DeepESN: Modular Deep Echo State Network](https://mlsanity.com/paper/1808.00523/) (2018)
- [Time-Varying Input and State Delay Compensation for Uncertain Nonlinear Systems](https://mlsanity.com/paper/1501.03810/) (2015)
- [Evolutionary Robust Clustering Over Time for Temporal Data](https://mlsanity.com/paper/2106.07252/) (2021)

## Built on by (most cited first, 7 of 7)

- [Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analytics](https://mlsanity.com/paper/2407.11038/) (2024)
- [Recurrent Stochastic Configuration Networks with Incremental Blocks](https://mlsanity.com/paper/2411.11303/) (2024)
- [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)
- [Deeper Insights into Learning Performance of Stochastic Configuration Networks](https://mlsanity.com/paper/2411.08544/) (2024)
- [Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling](https://mlsanity.com/paper/2412.00070/) (2024)
- [Kernel Stochastic Configuration Networks for Nonlinear Regression](https://mlsanity.com/paper/2412.05846/) (2024)

## Abstract (arXiv)

Temporal data modelling techniques with neural networks are useful in many domain applications, including time-series forecasting and control engineering. This paper aims at developing a recurrent version of stochastic configuration networks (RSCNs) for problem solving, where we have no underlying assumption on the dynamic orders of the input variables. Given a collection of historical data, we first build an initial RSCN model in the light of a supervisory mechanism, followed by an online update of the output weights by using a projection algorithm. Some theoretical results are established, including the echo state property, the universal approximation property of RSCNs for both the offline and online learnings, and the convergence of the output weights. The proposed RSCN model is remarkably distinguished from the well-known echo state networks (ESNs) in terms of the way of assigning the input random weight matrix and a special structure of the random feedback matrix. A comprehensive comparison study among the long short-term memory (LSTM) network, the original ESN, and several state-of-the-art ESN methods such as the simple cycle reservoir (SCR), the polynomial ESN (PESN), the leaky-integrator ESN (LIESN) and RSCN is carried out. Numerical results clearly indicate that the proposed RSCN performs favourably over all of the datasets.