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.
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References
Lonngren, K.E.: Notes to accompany a student laboratory experiment on chaos. IEEE Transactions on Education 34(1) (1991)
Matsumato, T., Chua, L., Tanaka, S.: Simplest Chaotic Nonautonomous Circuit. Phys. Rev. A 30, 1155–1157 (1984)
Azzouz, A., Hasler, M.: Orbits of the R-L-Diode Circuit. IEEE Transaction on Circuits and Systems 37, 1330–1339 (1990)
Aissi, C.: Introducing chaotic circuits in an undergraduate electronic course. In: Proceedings of the 2002 ASEE Gulf-Southwest Annual Conference,The University of Louisiana at Lafayette, March 20-22, 2002.Copyright © 2002, American Society for Engineering Education (2002)
de Moraes, R.M., Anlage, S.M.: Unified model and reverse recovery nonlinearities of the driven diode resonator. Phys. Rev. E. 68, 26–201 (2003)
Hanias, M.P., Giannaris, G., Spyridakis, A., Rigas, A.: Time series Analysis in chaotic diode resonator circuit. Chaos Solitons & fractals 27(2), 569–573 (2006)
Grassberger, P., Procaccia, I.: Characterization of strange attractors. Phys. Rev. Lett. 50, 346–349 (1983)
Grassberger, P., Procaccia, I.: Measuring the strangeness of strange attractors. Physica D 9, 189 (1983)
Hanias, M.P., Kalomiros, J.A., Karakotsou, C., Anagnostopoulos, A.N., Spyridelis, J.: Quasi-Periodic and Chaotic Self - Excited Voltage Oscillations in TlInTe2. Phys. Rev. B. 49, 16994 (1994)
Mozdy, E., Newell, T.C., Alsing, P.M., Kovanis, V., Gavrielides, A.: Synchronization and control in a unidirectionally coupled array of chaotic diode resonators. Physical Review E. 51(6), 5371–5376 (1995)
Abarbanel, H.D.I.: Analysis of Observed Chaotic Data. Springer, New York (1996)
Takens, F.: Lecture Notes in Mathematics 898 (1981)
Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (1997)
Aasen, T., Kugiumtzis, D., Nordahl, S.H.G.: Procedure for Estimating the Correlation Dimension of Optokinetic Nystagmus Signals. Computers and Biomedical Research 30, 95–116 (1997)
Fraser, A.M., Swinney, H.L.: Independent coordinates for strange attractors from mutual information. Phys. Rev. A. 33, 1134–1140 (1986)
Fraser, A.M.: IEEE transaction of information Theory 35, 245 (1989)
Kononov, E.: Virtual Recurrence Analysis, Version 4.9 (2006), (email:eugenek@ix.net.com.com)
Haykin, S.: Neural Networks, a comprehensive foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)
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Hanias, M.P., Karras, D.A. (2007). Efficient Non Linear Time Series Prediction Using Non Linear Signal Analysis and Neural Networks in Chaotic Diode Resonator Circuits. In: Perner, P. (eds) Advances in Data Mining. Theoretical Aspects and Applications. ICDM 2007. Lecture Notes in Computer Science(), vol 4597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73435-2_26
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DOI: https://doi.org/10.1007/978-3-540-73435-2_26
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