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.

开发适用于问题解决的随机配置网络的循环版本，无需对输入变量的动态顺序进行基本假设，并通过监督机制构建初始模型并通过投影算法在线更新输出权重，从而在时间数据建模技术方面取得了显着突破。