  This article introduces a robust hybrid method for solving supervised
learning tasks, which uses the Echo State Network (ESN) model and the Particle
Swarm Optimization (PSO) algorithm. An ESN is a Recurrent Neural Network with
the hidden-hidden weights fixed in the learning process. The recurrent part of
the network stores the input information in internal states of the network.
Another structure forms a free-memory method used as supervised learning tool.
The setting procedure for initializing the recurrent structure of the ESN model
can impact on the model performance. On the other hand, the PSO has been shown
to be a successful technique for finding optimal points in complex spaces.
Here, we present an approach to use the PSO for finding some initial
hidden-hidden weights of the ESN model. We present empirical results that
compare the canonical ESN model with this hybrid method on a wide range of
benchmark problems.
