MULTI-LAYER CORRECTIVE CASCADE ARCHITECTURE FOR ON-LINE PREDICTIVE ECHO STATE NETWORKS

摘要

An architecture for on-line learning of time series prediction is presented which uses a series of echo state networks (ESNs). Each ESN learns to predict an error correction term for the previous ESN. This technique is demonstrated to improve prediction accuracy for on-line learning of the Mackey-Glass chaotic oscillator. The results are compared to other architectural configurations to show that the improved performance emerges from sequential ESN error correction. A new recurrent network structure is shown to be a useful simplification of the usual ESN reservoir.