4 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling
Gang Dang, Dianhui Wang
Recurrent stochastic configuration networks (RSCNs) have shown great potential in modelling nonlinear dynamic systems with uncertainties. This paper presents an RSCN with hybrid re…
Recurrent Stochastic Configuration Networks with Incremental Blocks
Gang Dang, Dainhui Wang
Recurrent stochastic configuration networks (RSCNs) have shown promise in modelling nonlinear dynamic systems with order uncertainty due to their advantages of easy implementation,…
Deep Recurrent Stochastic Configuration Networks for Modelling Nonlinear Dynamic Systems
Gang Dang, Dianhui Wang
Deep learning techniques have shown promise in many domain applications. This paper proposes a novel deep reservoir computing framework, termed deep recurrent stochastic configurat…
Self-Organizing Recurrent Stochastic Configuration Networks for Nonstationary Data Modelling
Gang Dang, Dianhui Wang
Recurrent stochastic configuration networks (RSCNs) are a class of randomized learner models that have shown promise in modelling nonlinear dynamics. In many fields, however, the d…
Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analytics
Dianhui Wang, Gang Dang
This paper presents a novel neuro-fuzzy model, termed fuzzy recurrent stochastic configuration networks (F-RSCNs), for industrial data analytics. Unlike the original recurrent stoc…
Recurrent Stochastic Configuration Networks for Temporal Data Analytics
Dianhui Wang, Gang Dang
Temporal data modelling techniques with neural networks are useful in many domain applications, including time-series forecasting and control engineering. This paper aims at develo…