Evolutionary Echo State Network: evolving reservoirs in the Fourier space
Sebastian Basterrech, Gerardo Rubino

TL;DR
This paper introduces a novel ESN variant that evolves reservoir weights in the Fourier space using genetic algorithms, reducing dimensionality and enhancing modeling of non-linear dynamical systems.
Contribution
It proposes a Fourier space reservoir representation and genetic algorithm-based fine-tuning, offering a new approach to optimize ESN reservoirs efficiently.
Findings
Effective modeling of chaotic systems
Good performance on real-world data
Dimensionality reduction benefits
Abstract
The Echo State Network (ESN) is a class of Recurrent Neural Network with a large number of hidden-hidden weights (in the so-called reservoir). Canonical ESN and its variations have recently received significant attention due to their remarkable success in the modeling of non-linear dynamical systems. The reservoir is randomly connected with fixed weights that don't change in the learning process. Only the weights from reservoir to output are trained. Since the reservoir is fixed during the training procedure, we may wonder if the computational power of the recurrent structure is fully harnessed. In this article, we propose a new computational model of the ESN type, that represents the reservoir weights in the Fourier space and performs a fine-tuning of these weights applying genetic algorithms in the frequency domain. The main interest is that this procedure will work in a much smaller…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Neural Networks and Applications · Advanced Memory and Neural Computing
