Improved Multistep Nonlinear Time Series Prediction by applying Deterministic Chaos and Neural Network Techniques in Diode Resonator Circuits

Abstract

A novel non linear signal prediction method is presented using non linear signal analysis and deterministic chaos techniques in combination with neural networks for a diode resonator chaotic circuit. The Time series analysis is performed by the method proposed by Grasberger and Procaccia, involving estimation of the correlation and minimum embedding dimension as well as of the corresponding Kolmogorov entropy. These parameters are used to construct the first stage of a multistep predictor while a back-propagation artificial neural network (ANN) is involved in the second stage to enhance prediction results. The novelty of the proposed two stage predictor lies on involving a nearest / furthest neighbor prediction scheme in the first stage, integrated through a backpropagation ANN in the second stage. This novel two stage predictor is evaluated through an extensive experimental study.