Evolving chaotic neural systems for time series prediction

Abstract

We present a new type of neural architecture consisting of chaotic neurons and apply it to the prediction of chaotic time series signals. To evolve chaotic neural systems, we use cellular automata whose production rules are evolved based on a DNA coding method. The structure of networks are appropriate for learning nonlinear, chaotic, and nonstationary systems. In order to verify their effectiveness, we apply the evolutionary chaotic neural systems to one-step ahead prediction of Mackey-Glass time series data.