Multilayer reservoir computing to overcome the memory-nonlinearity trade-off

Reservoir Computing, a branch of Machine Learning, is a state-of-the-art research field because of its successful application to time-dependent computationally-hard tasks. Along this MSc Thesis, we will extend the basic concepts and features of Echo State Networks, which are a specific type of reservoir computers. The main goal of this work will be finding out a reservoir configuration for which the so-called memory-nonlinearity trade-off is overcome. In order to achieve such an enhancement, we will explore changes in the main properties of the reservoir, including its parameters, its degree of linearity and its topology.

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