Efficient non linear time series prediction using non linear signal analysis and neural networks in chaotic diode resonator circuits

Authors:
M. P. Hanias;D. A. Karras
Affiliations:
Chalkis Institute of Technology, Greece, Automation Dept., Hellas, Greece;Chalkis Institute of Technology, Greece, Automation Dept., Hellas, Greece
Venue:
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Year:
2007

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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. Multisim is used to simulate the circuit and show the presence of chaos. 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 one step / 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 that the backpropagation ANN is employed as a second order predictor, that is as an error predictor of the non-linear signal analysis stage application. This novel two stage predictor is evaluated through an extensive experimental study.