Cascade form predictor of neural and FIR filters and its minimum size estimation based on nonlinearity analysis of time series

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

Volumn E81-A, Issue 3, 1998, Pages 364-373

  Khalaf, Ashraf A M ^a   Nakayama, Kenji ^a  

^a KANAZAWA UNIVERSITY   (Japan)

Author keywords

Cascade form predictor; FIR filters; Input dimension estimation; Multilayer neural networks; Nonlinear prediction; Nonlinearity analysis; Time series prediction

Indexed keywords

ALGORITHMS; CASCADE CONNECTIONS; COMPUTER SIMULATION; DIGITAL FILTERS; LEARNING SYSTEMS; NEURAL NETWORKS; SET THEORY; TIME SERIES ANALYSIS;

CASCADE FORM PREDICTORS; INPUT DIMENSION ESTIMATION; LINEAR SUB PREDICTOR (LSP); NONLINEAR SUB PREDICTOR (NSP); TIME SERIES PREDICTION;

SIGNAL FILTERING AND PREDICTION;

EID: 0032023055     PISSN: 09168508     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (18)

1. A.S. Weigend and 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.
2. S. Haykin and L. Li, "Nonlinear adaptive prediction of nonstationary signals," IEEE Trans. Signal Processing, vol.43, no.2, pp.526-535, Feb. 1995.
3. A.S. Weigend and N.A. Gershenfeld, "Time series prediction: Forecasting the future and understanding the past," Proc. V. XV, Santa Fe Institute, 1994.
4. M. Kinouchi and M. Hagiwara, "Learning temporal sequences by complex neurons with local feedback," Proc. ICNN'95, pp.3165-3169, 1995.
5. T. Matsumoto, H. Hamagishi, and Y. Chonan, "A hierarchial bayes approach to nonlinear time series prediction with neural nets," Proc. ICNN'97, pp.2028-2033, 1997.
6. T.J. Cholewo and J.M. Zurada, "Sequential network construction for time series prediction," Proc. ICNN'97, pp.2034-2038, 1997.
7. A. Atia, N. Talaat, and S. Shaheen, "An efficient stock market forecasting model using neural networks," Proc. ICNN'97, pp.2112-2115, 1997.
8. P.M. Thiesing and O. Vornberger, "Sales forecasting using neural networks," Proc. ICNN'97, pp.2125-2128, 1997.
9. H. Tong and K.S. Lim, "Threshold autoregression, limit cycles and cyclical data," Journal Royal Statistical Society B, vol.42, pp.245-292, 1980.
10. W.-T. Lee and J. Pearrson, "A hybrid linear/nonlinear approach to channel equalization problems," Advances in Neural Information Processing Systems 5, eds. S.J. Hanson, J.D. Cowan, and C. Lee Giles, U.S.A. 1993.
11. S. Haykin and L. Li, "16kb/s adaptive differential pulse code modulation of speech," Proc. Int. Workshop Applications Neural Networks Telecommun, Princeton, NJ, pp.132-138, 1993.
12. A.A.M. Khalaf and K. Nakayama, "A hybrid neural predictor and its convergence analysis," Proc. of the 10th Karuizawa Workshop on Circuits and Systems, Japan, pp.357-362, April 1997.
13. A.A.M. Khalaf, K. Nakayama, and K. Hara, "A neuralFIR predictor: Minimum size estimation based on nonlinearity analysis of input sequence," Proc. ICANN'97, Lausanne, Switzerland, pp. 1047-1052, Oct. 1997.
14. K. Hara and K. Nakayama, "Comparison of signal classification performance between multilayer neural networks and linear signal processing methods," Information Processing Society of Japan Trans., vol.38, no.2, pp.245-259, Feb. 1997.
15. K. Hara and K. Nakayama, "Multy-frequency signal classification by multilayer neural networks and linear filter methods," IEICE Trans., Fundamental, vol.E80-A, no.5, pp.894-902, May 1997.
16. S. Haykin, "Neural Networks: A Comprehensive Foundation," Macmillan, New York, 1994.
17. J. Hertz, A. Krogh, and R.G. Palmer, "Introduction to the Theory of Neural Computation," Santa Fe Institute, 1991.
18. D.E. Rumelhart and J.L. McCelland, et al., "Parallel Distributed Processing," The MIT Press, pp.318-362, 1986.