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A Comparative Study of Reservoir Computing for Temporal Signal Processing

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Figure

Figure 1. Computation in a reservoir computer. The reservoir is made up of a dynamical neural network with randomly assigned weights
Figure 3. Scaling and optimization in the ESN. Figures 3(a), 3(b), and 3(c) show the training error surface of the ESN on the H´enon Map, the NARMA 10, and the NARMA 20 tasks respectively
Figure 4. Training and generalization RNMSE of the DL, the NARX network, and the ESN for three different tasks
Figure 5. Figure 5(a) shows how the complexity of the DL compares with the ESN of identical memorization performance

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