Deep Recurrent Stochastic Configuration Networks for Modelling Nonlinear Dynamic Systems
This is an incremental improvement for researchers in machine learning and engineering, focusing on enhancing reservoir computing for dynamic system modeling.
The paper tackles modeling nonlinear dynamic systems by proposing a deep recurrent stochastic configuration network (DeepRSCN), which outperforms single-layer networks in efficiency, learning capability, and generalization across time series prediction, system identification, and industrial data tasks.
Deep learning techniques have shown promise in many domain applications. This paper proposes a novel deep reservoir computing framework, termed deep recurrent stochastic configuration network (DeepRSCN) for modelling nonlinear dynamic systems. DeepRSCNs are incrementally constructed, with all reservoir nodes directly linked to the final output. The random parameters are assigned in the light of a supervisory mechanism, ensuring the universal approximation property of the built model. The output weights are updated online using the projection algorithm to handle the unknown dynamics. Given a set of training samples, DeepRSCNs can quickly generate learning representations, which consist of random basis functions with cascaded input and readout weights. Experimental results over a time series prediction, a nonlinear system identification problem, and two industrial data predictive analyses demonstrate that the proposed DeepRSCN outperforms the single-layer network in terms of modelling efficiency, learning capability, and generalization performance.