Recurrent Stochastic Configuration Networks With Hybrid Regularization for Nonlinear Dynamics Modeling
提出一种混合正则化的随机配置递归网络，先用LASSO识别关键变量，再用L2正则化网络逼近残差，通过投影算法实时更新输出权重，在非线性系统辨识和工业预测任务中表现优于其他模型。
Recurrent stochastic configuration networks (RSCNs) have shown great potential in modeling nonlinear dynamic systems with uncertainties. This article presents an RSCN with hybrid regularization to enhance both the learning capacity and generalization performance of the network. Given a set of temporal data, the well-known least absolute shrinkage and selection operator (LASSO) is employed to identify the significant order variables. Subsequently, an improved RSCN with L2 regularization is introduced to approximate the residuals between the output of the target plant and the LASSO model. The output weights are updated in real-time through a projection algorithm, facilitating a rapid response to dynamic changes within the system. A theoretical analysis of the universal approximation property is provided, contributing to the understanding of the network's effectiveness in representing various complex nonlinear functions. Experimental results from a nonlinear system identification problem and two industrial predictive tasks demonstrate that the proposed method outperforms other models across all testing datasets.