PSO for reservoir computing optimization

Abstract

Reservoir Computing is a paradigm of artificial neural networks that has obtained promising results. However there are some disadvantages: the reservoir is created randomly and needs to be large enough to be able to capture all the features of the data. For this work we use PSO - Particle Swarm Optimization to optimize the initial parameters of the Reservoir Computing. The results obtained with the optimization method are compared with results obtained by an exhaustive search for global parameters generation of Reservoir Computing. Five time series were used to show that the optimization method reduces the number of training cycles required to train the system. © 2012 Springer-Verlag.