Echo State Networks and Neural Network Ensembles to predict Sunspots activity
Friedhelm Schwenker, Amr Hussein Labib · 2009
Abstract. Echo state networks (ESN) and ensembles of neural networks are developed for the prediction of the monthly sunspots series. Through numerical evaluation on this benchmark data set it has been shown that the feedback ESN models outperform feedforward MLP. Furthermore, it is shown that median fusion lead to robust predictors, and even can improve the prediction accuracy of the best individual predictors. 1 Echo State Networks The echo state network (ESN) is a recurrent neural network model trained using supervised learning [1, 2, 3]. In the following we present a brief introduction to the ESN architecture and ESN learning. The ESN Network Model Each neuron, or unit, of the network has an activation state at a given time step n. The network consists of a set of K input units with an activation vector u(n), a set of N inner units with an activation vector x(n), and a set of L output units with an activation vector y(n) [3].