Abstract
A hybrid nonlinear time series predictor that consists a nonlinear subpredictor (NSP) and a linear sub-predictor (LSP) combined in a cascade form is proposed. A multilayer neural network is employed as the NSP and the algorithm used to update the NSP weights is Lyapunov stability-based backpropagation algorithm (LABP). The NSP can predict the nonlinearity of the input time series. The NSP prediction error is then further compensated by employing a LSP. Weights of the LSP are adaptively adjusted by the Lyapunov adaptive algorithm. Signals’ stochastic properties are not required and the error dynamic stability is guaranteed by the Lyapunov Theory. The design of this hybrid predictor is simplified compared to existing hybrid or cascade neural predictors [1]-[2]. It is fast convergence and less computation complexity. The theoretical prediction mechanism of this hybrid predictor is further confirmed by simulation examples for real world data.
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References
Simon Haykin, “Nonlinear Adaptive Prediction of Nonstationary Signals”, IEEE Trans. Signal Processing, Vol. 43, No. 2, February, 1995.
Jens Baltersee, Jonathon A. Chambers, “Nonlinear Adaptive Prediction of Speech With A Pipeline Recurrent Neural Network”, IEEE Trans. Signal Processing, Vol. 46, No. 8, August, 1998.
Ashraf A. M. K, Kenji N, “A Cascade Form Predictor of Neural and FIR Filters and Its Minimum Size Estimation Based on Nonlinearity Analysis of Time Series”, IEICE Trans. Fundamentals, Vol. E81-A, No. 3, March, 1998.
Ashraf A. M. K, Kenji N, “A Hybrid Nonlinear Predictor: Analysis of Learning Process and Predictability for Noisy Time Series”, IEICE Trans. Fundamentals, Vol. E82-A, No. 8, August, 1999.
A. S. Weigend, D. E. Rumelhart, “Generalization through minimal networks with application to forecasting”, Proc. INTERFACE’91: Computing Science and Statistics, ed. Elaine Keramindas, pp. 362–370, Springer Verlag, 1992.
Steve Lawrence, Ah Chung Tsoi, C. Lee Giles, “Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference”, Technical report, UMIACSTR-96-27 and CS-TR-3625, Institute for Advanced Computer Studies, University of Maryland.
A. S. Weigend, N. A. Gershenfeld, “Times series prediction: Forecasting the future and understanding the past”, Proc. V. XV, Santa Fe Institute, 1994.
Zhihong Man, Seng Kah Phooi, H. R. Wu, “Lyapunov stability-based adaptive backpropagation for discrete and continuous time systems”, 2nd International Conference on Information, Communications and signal processing, ICICS’99, pp. paper no. 376, 1999.
Man ZhiHong, H. R. Wu, W. Lai and Thong Nguyen, “Design of Adaptive Filters Using Lyapunov Stability Theory”, The 6th IEEE International Workshop on Intelligent Signal Processing and Communication Systems, vo1, pp. 304–308, 1998.
Slotine, J-J. E. and Li, W. Applied nonlinear control, Prentice-Hall, Englewood Cliffs, NJ, 1991.
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Phooi, S.K., Zhihong, M., Wu, H.R. (2000). Nonlinear and Noisy Time Series Prediction Using a Hybrid Nonlinear Neural Predictor. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_29
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DOI: https://doi.org/10.1007/3-540-44491-2_29
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