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Neuro-Fuzzy Network Based On Extended Kalman Filtering for Financial Time Series

Authors: Chokri Slim


The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy architecture based on Extended Kalman filter. To test the performance and applicability of the proposed neuro-fuzzy model, simulation study of nonlinear complex dynamic system is carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction of financial time series. A benchmark case studie is used to demonstrate that the proposed model is a superior neuro-fuzzy modeling technique.

Keywords: Nonlinear Systems, extended Kalman filter, financial time series, neuro-fuzzy

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[1] J.J. Buckley, Y. Hayashi, Fuzzy neural networks: a survey, Fuzzy Sets and Systems 66 pp 1-13 1994
[2] S.Chokri, T Abdelwahed, Neural Network for Modeling Nonlinear Time Series: A New Approach. Springer-Verlag Berlin Lecture Note in Computing Science. 2659 pp 159-168 2003.
[3] H. Takagi, Fusion techniques of fuzzy systems and neural networks, and fuzzy systems and genetic algorithms, SPIE 2061 pp 402-413 1995.
[4] J.-S.Jang,ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans.Systems,Man, Cybernet.23 pp 665ÔÇö685 1993.
[5] Eiji Mizutani,J.-S.Jang,Coactive neural fuzzy modeling, in:Proc.of IEEE Internat.Conf. on Neural Networks, Vol.2,Perth,Australia, 760 -765 1995.
[6] M.F.Azeem,et al., Generalization of adaptive neuro-fuzzy inference systems, IEEE Trans.Neural Networks 11 pp 1332 -1346 2000.
[7] Shin-ichi Horikawa,et al., On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm, IEEE Trans.Neural Networks 3 pp 801 -806 1992.
[8] G. Puskorius, L. Feldkamp, Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks, IEEE Trans. Neural Networks 5 pp 279ÔÇö297 1994.
[9] L. Zadeh. Fuzzy Sets, Inf. Control, vol 8 pp 338-353 1965.
[10] Mamdani, E. H. and S. Assilian. ,"An experiment in linguistic synthesis with a fuzzy logic controller." Int. J. Man-Machine Studies 7 1975.
[11] S. Shah, F. Palmieri, M. Datum, Optimal ltering algorithms for fast learning in feedforward neural Networks, Neural Networks 5 pp 779ÔÇö 787 1992.
[12] Kalman, R. E., " A New Approach to linear Filtering and Prediction Problems," Transaction of ASME-Journal of basic Engineering, pp 35- 45 1960.
[13] Aoki, M. , State-Space Modeling of Time Series, Berlin: Springer- Verlag, 1987.
[14] S. Singhal, L. Wu, Training multilayer perceptrons with the extended Kalman algorithm, in: D. Touretzky Ed.,Advances in Neural Information Processing Systems, Vol. 1. Morgan Kaufmann, San Mateo, CA, pp 133-140 1989.