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NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches. (English) Zbl 1058.62077

Summary: The nonlinear autoregressive moving average with exogenous inputs (NARMAX) model provides a powerful representation for time series analysis, modeling and prediction due to its capability of accommodating the dynamic, complex and nonlinear nature of real-world time series prediction problems. This paper focuses on modeling and prediction of NARMAX-model-based time series using the fuzzy neural network (FNN) methodology. Both feedforward and recurrent FNNs approaches are proposed. Furthermore, an efficient algorithm for model structure determination and parameter identification with the aim of producing improved predictive performance for NARMAX time-series models is developed. Experiments and comparative studies demonstrate that the proposed FNN approaches can effectively learn complex temporal sequences in an adaptive way and outperform some well-known existing methods.

MSC:

62M20 Inference from stochastic processes and prediction
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
93C42 Fuzzy control/observation systems
62M45 Neural nets and related approaches to inference from stochastic processes
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[1] Box, G. E.P.; Jenkins, G. M., Time Series Analysis, Forecasting and Control (1970), Holden Day: Holden Day San Francisco, CA · Zbl 0109.37303
[2] Chao, C. T.; Chen, Y. J.; Teng, C. C., Simplification of fuzzy-neural systems using similarity analysis, IEEE Trans. Systems, Man Cybernet., 26, 344-354 (1996)
[3] Chen, S.; Cowan, C. F.N.; Grant, P. M., Orthogonal least squares learning algorithm for radial basis function network, IEEE Trans. Neural Networks, 2, 302-309 (1991)
[4] Cho, K. B.; Wang, B. H., Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction, Fuzzy Sets and Systems, 83, 325-339 (1996)
[5] Connor, J. T.; Martin, R. D.; Atlas, L. E., Recurrent neural networks and robust time series prediction, IEEE Trans. Neural Networks, 5, 240-254 (1994)
[6] Gao, Y.; Er, M. J., Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems, IEEE Trans. Fuzzy Systems, 11, 462-477 (2003)
[7] Y. Gao, M.J. Er, An intelligent adaptive control scheme for postsurgical blood pressure regulation, IEEE Trans. Neural Networks, 16 (2004).; Y. Gao, M.J. Er, An intelligent adaptive control scheme for postsurgical blood pressure regulation, IEEE Trans. Neural Networks, 16 (2004).
[8] Jang, J. S.R., ANFISadaptive-network-based fuzzy inference system, IEEE Trans. Systems Man Cybernet., 23, 665-684 (1993)
[9] Juang, C. F., Temporal problems solved by dynamic fuzzy network based on genetic algorithm with variable-length chromosomes, Fuzzy Sets and Systems, 142, 199-219 (2004) · Zbl 1081.68085
[10] Kasabov, N. K.; Song, Q., DENFISdynamic evolving neural-fuzzy inference system and its application for time-series prediction, IEEE Trans. Fuzzy Systems, 10, 144-154 (2002)
[11] Kim, J.; Kasabov, N., HyFISadaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems, Neural Networks, 12, 1301-1319 (1999)
[12] C.J. Lin, C.H. Chen, Identification and prediction using recurrent compensatory neuro-fuzzy systems, Fuzzy Sets and Systems, Available online 11 August 2004, in press.; C.J. Lin, C.H. Chen, Identification and prediction using recurrent compensatory neuro-fuzzy systems, Fuzzy Sets and Systems, Available online 11 August 2004, in press. · Zbl 1067.68129
[13] Mastorocostas, P. A.; Theocharis, J. B., An orthogonal least-squares method for recurrent fuzzy-neural modeling, Fuzzy Sets and Systems, 140, 285-300 (2003) · Zbl 1036.93037
[14] Papadakis, S. E.; Theocharis, J. B.; Bakirtzis, A. G., A load curve based fuzzy modeling technique for short-term load forecasting, Fuzzy Sets and Systems, 135, 279-303 (2003) · Zbl 1031.93011
[15] Press, W. H.; Teukolsky, S. A.; Vetterling, W. T.; Flannery, B. P., Numerical Recipes in CThe Art of Scientific Computing (1992), University Press: University Press Nancy · Zbl 0845.65001
[16] Rojas, I.; Gonzalez, J.; Canas, A.; Diaz, A. F.; Rojas, F. J.; Rodriguez, M., Short-term prediction of chaotic time series by using RBF network with regression weights, Internat. J. Neural Systems, 10, 353-364 (2000)
[17] Salmeron, M.; Ortega, J.; Puntonet, C. G.; Prieto, A., Improved RAN sequential prediction using orthogonal techniques, Neurocomputing, 41, 153-172 (2001) · Zbl 0995.68087
[18] Wang, L. X., A Course in Fuzzy Systems and Control (1997), Prentice-Hall: Prentice-Hall Englewood Cliffs, NJ
[19] Wu, S.; Er, M. J., Dynamic fuzzy neural networks—a novel approach to function approximation, IEEE Trans. Systems Man Cybernet. Part B, 30, 358-364 (2000)
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