Fig. 6
From: Input driven optimization of echo state network parameters for prediction on chaotic time series
: The network architecture consists of training and inference phases. The training phase shows the dimensional transformation of data through the network, where each block’s notation (e.g., 7 -> 256) indicates its input and output dimensions. The inference phase demonstrates the iterative optimization process using the trained network with fixed weights to discover optimal parameters.
