Analysis of prediction performance in wavelet minimum complexity echo state network

Abstract

Echo state network (ESN) has become one of the most popular recurrent neural networks (RNN) for its good prediction performance of non-linear time series and simple training process. But several problems still prevent ESN from becoming a widely used tool. The most prominent problem is its high complexity with lots of random parameters. Aiming at this problem, a minimum complexity ESN model (MCESN) was proposed. In this paper, we proposed a new wavelet minimum complexity ESN model (WMCESN) to improve the prediction accuracy and increase the practical applicability. Our new model inherits the characters of minimum complexity ESN model using the fixed parameters and simple circle topology. We injected wavelet neurons to replace the original neurons in internal reservoir and designed a wavelet parameter matrix to reduce the computing time. By using different datasets, our new model performed better than the minimum complexity ESN model with normal neurons, but only utilized tiny time cost. We also used our own packets of transmission control protocol (TCP) and user datagram protocol (UDP) dataset to prove that our model can deal with the data packet bit prediction problem well.