Identification of motion with echo state network

The control of an underwater robot is difficult due to non-linear effects. Echo State Networks (ESNs) provide a way of dealing with non-linearity. The quality of an Echo State Network (ESN) (H. Jaeger, 2001, 2002) depends strongly on its topology. Usually an educated guess combined with a brute force method is used to obtain an ESN that, after training, produces a low error. In this article the authors suggest another way to find good topologies by using evolution. Two heuristics, evolutionary algorithms and evolutionary strategies, are compared. The proposed method outperforms standard ones like ARX and back-propagation networks on the data from the Twin-Burger underwater robot.