Clustered Echo State Networks for Signal Observation and Frequency Filtering

Abstract

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.