Shared reservoir modular echo state networks for chaotic time series prediction

This paper proposes a new RNN - shared reservoir modular echo-state networks (SRMESNs), which has a higher forecast precision when the amount of training data is large enough. First, the neural state space is divided into several subspaces. And then the data belonging to each subspace is put into the same reservoir. But for each subspace, we set up an independent output weight vector respectively. So it combines the advantages of ESNs and modularization. The method is tested on the benchmark prediction problem of Mackey-Glass time series, and the result shows that the methodology proposed is efficient.