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Fast and effective tuning of Echo State Network reservoir parameters using evolutionary algorithms and template matrices

Echo State Network (ESN) is a special type of neural network with a randomly generated structure called the reservoir. The performance of ESN is sensitive to the reservoir parameters, which have to be tuned for best performance. Tuning of the reservoir parameters using evolutionary algorithms can be slow and produce inconsistent results. In this paper, we present a simple method for generating reservoirs based on templates that makes the reservoir matrices deterministic with respect to the parameters. Compared with the traditional method where the reservoir matrices are random, tuning of the reservoir parameters with an evolutionary algorithm needs less time, less number of cost function evaluations, and produces more reliable results using the proposed method.