Clustered Echo State Networks for Signal Observation and Frequency Filtering

  • Laércio Oliveira Junior USP
  • Florian Stelzer Humboldt University of Berlin / Technical University of Berlin
  • Liang Zhao USP


Echo State Networks (ESNs) are recurrent neural networks that map an input signal to a high-dimensional dynamical system, called reservoir, and possess adaptive output weights. The output weights are trained such that the ESN’s output signal fits the desired target signal. Classical reservoirs are sparse and randomly connected networks. In this article, we investigate the effect of different network topologies on the performance of ESNs. Specifically, we use two types of networks to construct clustered reservoirs of ESN: the clustered Erdös–Rényi and the clustered Barabási-Albert network model. Moreover, we compare the performance of these clustered ESNs (CESNs) and classical ESNs with the random reservoir by employing them to two different tasks: frequency filtering and the reconstruction of chaotic signals. By using a clustered topology, one can achieve a significant increase in the ESN’s performance.

Palavras-chave: clustered networks, complex networks, echo state networks, machine learning, neural networks


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OLIVEIRA JUNIOR, Laércio; STELZER, Florian; ZHAO, Liang. Clustered Echo State Networks for Signal Observation and Frequency Filtering. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 8. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 25-32. ISSN 2763-8944. DOI: