Effect of memory capacity characteristics on time-series prediction performance of reservoir neural network with extended chaotic neural network model

Abstract

We examined the memory capacity (MC) characteristics of a reservoir neural network (RNN) that uses an extended chaotic neural network (ExCNN) model as the reservoir layer. Furthermore, we propose a novel prediction performance measure based on the long tail property of MC. To design RNN using the ExCNN model, we derived the echo state property of the ExCNN model. To evaluate the effect of exponential local memory terms of the ExCNN model on prediction performance, we performed a closed-loop one-step prediction of a periodic random sequence. The results indicate that RNN using the ExCNN model with high prediction performance exhibits a distinctive long tail in MC characteristic curve with respect to a calculation delay.