Echo State Network Optimization: A Systematic Literature Review

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Abstract In the recent years, numerous studies have demonstrated the importance and efficiency of Reservoir Computing (RC) approaches. The choice of parameters and architecture in reservoir computing, on the other hand, frequently leads to an optimization task. This paper attempts to present an overview of the related work on Echo State Network (ESN) optimization and to collect research papers through a systematic Literature Review (SLR). This review covers 74 items published since 2004 and 2021 that are concerned with the issue of our focus. The collected papers are selected, analysed and reported. The results indicate that there are two techniques of parameters optimization (bio-inspired and non-bio-inspired methods) have been extensively used for various reasons. But the bio-inspired ones are the greatest. The potential use of Particle Swarm Optimization (PSO) has also been noted. A significant portion of the research done in this field focuses on the study of reservoirs and how they behave in relation to their unique qualities. In order to test reservoirs with varied parameters, topologies, or training techniques, NARMA, the Mackey glass, and Lorenz time-series prediction dataset are the most commonly employed in the literature. This research debate diverse point of view about ESN's hyper-parameter optimization, metrics, datasets, evaluation measures, and bio-inspired and non-bio-inspired techniques, this paper identifies and explores a number of research gaps.

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