메뉴 건너뛰기




Volumn E82-A, Issue 8, 1999, Pages 1420-1427

A hybrid nonlinear predictor: analysis of learning process and predictability for noisy time series

Author keywords

Fir filter; Neural network; Noise robustness; Nonlinear; Prediction; Time series

Indexed keywords

COMPUTER SIMULATION; FIR FILTERS; MATHEMATICAL MODELS; MULTILAYER NEURAL NETWORKS; TIME SERIES ANALYSIS;

EID: 0032596319     PISSN: 09168508     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (4)

References (15)
  • 4
    • 0029252331 scopus 로고
    • Nonlinear adaptive prediction of nonstationary signals
    • Feb.
    • S. Haykin and L. Li, Nonlinear adaptive prediction of nonstationary signals, IEEE Trans. Signal Processing, vol.43, no.2, pp.526-535, Feb. 1995.
    • (1995) IEEE Trans. Signal Processing, Vol.43, No. , pp. 526-535
    • Haykin, S.1    Li, L.2
  • 6
    • 0029548014 scopus 로고
    • Learning temporal sequences by complex neurons with local feedback
    • M. Kinouchi and M. Hagiwara, Learning temporal sequences by complex neurons with local feedback, Proc. ICNN'95, pp.3165-3169, 1995.
    • (1995) Proc. ICNN'95 , pp. 3165-3169
    • Kinouchi, M.1    Hagiwara, M.2
  • 7
    • 0030702720 scopus 로고    scopus 로고
    • A hierarchial bayes approach to nonlinear time series prediction with neural nets
    • 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.
    • (1997) Proc. ICNN'97 , pp. 2028-2033
    • Matsumoto, T.1    Hamagishi, H.2    Chonan, Y.3
  • 8
    • 0030688746 scopus 로고    scopus 로고
    • Sequential network construction for time series prediction
    • T.J. Cholewo and J.M. Zurada, Sequential network construction for time series prediction, Proc. ICNN'97, pp.2034-2038, 1997.
    • (1997) Proc. ICNN'97 , pp. 2034-2038
    • Cholewo, T.J.1    Zurada, J.M.2
  • 10
    • 0031271851 scopus 로고    scopus 로고
    • Power prediction in mobile communication systems using an optimal neural-network structure
    • Nov.
    • X.M. Gao, X.Z. Gao, J.M.A. Tanskanen, and S.J. Ovaska, Power prediction in mobile communication systems using an optimal neural-network structure, IEEE Trans. Neural Networks, vol.8, no.C, pp.1446-1455, Nov. 1997.
    • (1997) IEEE Trans. Neural Networks , vol.8 , pp. 1446-1455
    • Gao, X.M.1    Gao, X.Z.2    Tanskanen, J.M.A.3    Ovaska, S.J.4
  • 11
    • 84956611057 scopus 로고    scopus 로고
    • A neural-FIR predictor: Minimum size estimation based on nonlinearity analysis of input sequence
    • Lausanne, Switzerland, Oct.
    • A.A.M. Khalaf, K. Nakayama, and K. Hara, A neural-FIR predictor: Minimum size estimation based on nonlinearity analysis of input sequence, Proc. ICANN'97, pp.1047-1052, Lausanne, Switzerland, Oct. 1997.
    • (1997) Proc. ICANN'97 , pp. 1047-1052
    • Khalaf, A.A.M.1    Nakayama, K.2    Hara, K.3
  • 12
    • 0032023055 scopus 로고    scopus 로고
    • A cascade form predictor of neural and FIR filters and its minimum size estimation based on nonlinearity analysis of time series
    • March
    • A.A.M. Khalaf and K. Nakayama, A cascade form predictor of neural and FIR filters and its minimum size estimation based on nonlinearity analysis of time series, IEICE Trans. Fundamental, vol.E81-A, no.3, pp.364-373, March 1998.
    • (1998) IEICE Trans. Fundamental, Vol.E81-A, No.3 , pp. 364-373
    • Khalaf, A.A.M.1    Nakayama, K.2
  • 15
    • 0000003175 scopus 로고
    • Threshold autoregression, limit cycles and cyclical data
    • H. Tong and K.S. Lim, Threshold autoregression, limit cycles and cyclical data, Journal Royal Statistical Society B, vol.42, pp.245-292, 1980.
    • (1980) Journal Royal Statistical Society B, Vol. , vol.42 , pp. 245-292
    • Tong, H.1    Lim, K.S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.