<ul><li>This paper introduces the concept of recurrent stochastic configuration networks (RSCNs) for temporal data analytics.</li><li>RSCNs are developed to solve problems in domains like time-series forecasting and control engineering.</li><li>The RSCN model is different from the well-known echo state networks (ESNs) and has unique properties.</li><li>Numerical results show that the proposed RSCN performs favorably compared to other methods.</li></ul>

Recurrent Stochastic Configuration Networks for Temporal Data Analytics

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