메뉴 건너뛰기




Volumn 40, Issue 5, 1994, Pages 1474-1489

Dynamic System Identification with Order Statistics

Author keywords

Nonlinear system identification; nonparametric regression; order statistics; orthogonal series; spacings

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; ERRORS; ESTIMATION; FOURIER TRANSFORMS; PROBABILITY DENSITY FUNCTION; REGRESSION ANALYSIS; SIGNAL PROCESSING; STATISTICS;

EID: 0028514192     PISSN: 00189448     EISSN: 15579654     Source Type: Journal    
DOI: 10.1109/18.333862     Document Type: Article
Times cited : (24)

References (41)
  • 1
    • 0000115649 scopus 로고
    • Non-strong mixing autoregressive processes
    • D. W. K. Andrews, “Non-strong mixing autoregressive processes,” J. Appl. Prob., vol. 21, pp. 930-934, 1984.
    • (1984) J. Appl. Prob. , vol.21 , pp. 930-934
    • Andrews, D.W.K.1
  • 2
    • 17944379407 scopus 로고
    • A nearly independent, but non-strong mixing, triangular array
    • —, “A nearly independent, but non-strong mixing, triangular array,” J. Appl. Prob., vol. 22, pp. 729-731, 1985.
    • (1985) J. Appl. Prob. , vol.22 , pp. 729-731
  • 5
    • 0019082388 scopus 로고
    • Identification of nonlinear systems—A survey
    • S. A. Billings, “Identification of nonlinear systems—A survey,” Proc. IEE, vol. 127, pp. 272-285, 1980.
    • (1980) Proc. IEE , vol.127 , pp. 272-285
    • Billings, S.A.1
  • 6
    • 0017699409 scopus 로고
    • The identification of a particular nonlinear time series system
    • D. R. Brillinger, “The identification of a particular nonlinear time series system,” Biometrika, vol. 64, pp. 509-515, 1977.
    • (1977) Biometrika , vol.64 , pp. 509-515
    • Brillinger, D.R.1
  • 7
    • 84972525317 scopus 로고
    • Choosing a kernel regression estimator
    • C. K. Chu and J. S. Marron, “Choosing a kernel regression estimator,” Statist. Sci., vol. 6, pp. 404-436, 1991.
    • (1991) Statist. Sci. , vol.6 , pp. 404-436
    • Chu, C.K.1    Marron, J.S.2
  • 11
    • 0022103578 scopus 로고
    • Nonparametric kernel algorithm for recovering of functions from noisy measurement with applications
    • A. Georgiev,”Nonparametric kernel algorithm for recovering of functions from noisy measurement with applications,” IEEE Trans. Automat. Contr., vol. AC-30, pp. 782-784, 1985.
    • (1985) IEEE Trans. Automat. Contr. , vol.AC-30 , pp. 782-784
    • Georgiev, A.1
  • 12
    • 0024909261 scopus 로고
    • Non-parametric orthogonal series identification of Hammerstein systems
    • W. Greblicki, “Non-parametric orthogonal series identification of Hammerstein systems,” Int. J. Syst. Sci., vol. 20, pp. 2355-2367, 1989.
    • (1989) Int. J. Syst. Sci. , vol.20 , pp. 2355-2367
    • Greblicki, W.1
  • 13
    • 51249178643 scopus 로고
    • Fourier and Hermite series estimates of regression functions
    • W. Greblicki and M. Pawlak, “Fourier and Hermite series estimates of regression functions,” Ann. Inst. Statist. Math., vol. 37, pp. 443-459, 1985.
    • (1985) Ann. Inst. Statist. Math. , vol.37
    • Greblicki, W.1    Pawlak, M.2
  • 14
    • 0024628999 scopus 로고
    • Nonparametric identification of Hammerstein systems
    • —, “Nonparametric identification of Hammerstein systems,” IEEE Trans. Inform. Theory, vol. 35, pp. 409-418, 1989.
    • (1989) IEEE Trans. Inform. Theory , vol.35 , pp. 409-418
  • 15
    • 0025792340 scopus 로고
    • Nonparametric identification of a cascade nonlinear time series system
    • —, “Nonparametric identification of a cascade nonlinear time series system,” Signal Processing, vol. 22, pp. 61-75, 1991.
    • (1991) Signal Processing , vol.22 , pp. 61-75
  • 16
    • 0026842725 scopus 로고
    • Nonparametric identification of a particular nonlinear time series system
    • —, “Nonparametric identification of a particular nonlinear time series system,” IEEE Trans. Signal Processing, vol. 40, pp. 985-989, 1992.
    • (1992) IEEE Trans. Signal Processing , vol.40 , pp. 985-989
  • 17
    • 0026915065 scopus 로고
    • Nonparametric identification of Wiener systems
    • W. Greblicki, “Nonparametric identification of Wiener systems,” IEEE Trans. Inform. Theory, vol. 38, 1487-1493, 1992.
    • (1992) IEEE Trans. Inform. Theory , vol.38
    • Greblicki, W.1
  • 20
    • 0000944497 scopus 로고
    • A geometric method for removing edge effects from kernel-type nonparametric regression estimators
    • Sept.
    • P. Hall and T. E. Wehrly, “A geometric method for removing edge effects from kernel-type nonparametric regression estimators,” J. Amer. Statist. Ass., vol. 86, pp. 665-672, Sept. 1991.
    • (1991) J. Amer. Statist. Ass. , vol.86 , pp. 665-672
    • Hall, P.1    Wehrly, T.E.2
  • 21
    • 0038021091 scopus 로고
    • A law of the iterated logarithm for nonparametric regression function estimators
    • W. Härdle, “A law of the iterated logarithm for nonparametric regression function estimators,” Ann. Statist., vol. 12, pp. 624-635, 1984.
    • (1984) Ann. Statist. , vol.12 , pp. 624-635
    • Härdle, W.1
  • 22
  • 23
    • 84981405040 scopus 로고
    • Kernel regression smoothing of time series
    • W. Härdle and P. Vieu, “Kernel regression smoothing of time series,” J. Time Series, vol. 13, pp. 209-232, 1992.
    • (1992) J. Time Series , vol.13 , pp. 209-232
    • Härdle, W.1    Vieu, P.2
  • 24
    • 0026220376 scopus 로고
    • Memoryless nonlinear system identification with unknown model order
    • R. Kannurpatti and G. W. Hart, “Memoryless nonlinear system identification with unknown model order,” IEEE Trans. Inform. Theory, vol. 37, pp. 1440-1450, 1991.
    • (1991) IEEE Trans. Inform. Theory , vol.37 , pp. 1440-1450
    • Kannurpatti, R.1    Hart, G.W.2
  • 25
    • 0024738273 scopus 로고
    • Identification of discrete Hammerstein systems by the Fourier series regression estimate
    • A. Krzyzak, “Identification of discrete Hammerstein systems by the Fourier series regression estimate,” Int. J. Syst. Sci., vol. 20, pp. 1729-1744, 1989.
    • (1989) Int. J. Syst. Sci. , vol.20 , pp. 1729-1744
    • Krzyzak, A.1
  • 26
    • 0025234172 scopus 로고
    • On estimation of a class of nonlinear systems by the kernel regression estimate
    • –—, “On estimation of a class of nonlinear systems by the kernel regression estimate,” IEEE Trans. Inform. Theory, vol. 36, pp. 141-152, 1990.
    • (1990) IEEE Trans. Inform. Theory , vol.36 , pp. 141-152
  • 27
    • 0001462696 scopus 로고
    • Asymptotic optimality of Cp,CL, cross-validation and generalized cross-validation: Discrete index set
    • K. C. Li “Asymptotic optimality of Cp,CL, cross-validation and generalized cross-validation: Discrete index set,” Ann. Statis., vol. 15, pp. 958-975, 1987.
    • (1987) Ann. Statis. , vol.15 , pp. 958-975
    • Li, K.C.1
  • 28
    • 0000765526 scopus 로고
    • Fourier-Koeffizienten und Funktionenklassen
    • G. G. Lorentz, “Fourier-Koeffizienten und Funktionenklassen,” Math. Z., vol. 51, pp. 135-149, 1948.
    • (1948) Math. Z. , vol.51 , pp. 135-149
    • Lorentz, G.G.1
  • 29
    • 45449124975 scopus 로고
    • Convolution type estimators for nonparametric regression
    • Y. P. Mack and H. G. Muller, “Convolution type estimators for nonparametric regression,” Statis. Prob. Lett., vol. 7, pp. 229-239, 1989.
    • (1989) Statis. Prob. Lett. , vol.7 , pp. 229-239
    • Mack, Y.P.1    Muller, H.G.2
  • 31
    • 0026171066 scopus 로고
    • On the series expansion approach to the identification of Hammerstein system
    • M. Pawlak, “On the series expansion approach to the identification of Hammerstein system,” IEEE Trans. Automat. Contr., vol. 36, pp. 763-767, 1991.
    • (1991) IEEE Trans. Automat. Contr. , vol.36 , pp. 763-767
    • Pawlak, M.1
  • 32
  • 34
    • 38249037345 scopus 로고
    • Nonparametric orthogonal series estimators of regression: A class attaining the optimal convergence rate in L2
    • E. Rafajlowicz, “Nonparametric orthogonal series estimators of regression: A class attaining the optimal convergence rate in L2,” Statist. Prob. Lett., vol. 5, pp. 219-224, 1987.
    • (1987) Statist. Prob. Lett. , vol.5 , pp. 219-224
    • Rafajlowicz, E.1
  • 36
    • 0026014267 scopus 로고
    • Identification of MISO nonlinear regression in the presence of a wide class of disturbances
    • L. Rutkowski, “Identification of MISO nonlinear regression in the presence of a wide class of disturbances,” IEEE Trans. Inform. Theory, vol. 37, pp. 214-216, 1991.
    • (1991) IEEE Trans. Inform. Theory , vol.37 , pp. 214-216
    • Rutkowski, L.1
  • 37
    • 0000388992 scopus 로고
    • Consistent nonparametric regression
    • C. J. Stone, “Consistent nonparametric regression,” Ann. Statist., vol. 5, pp. 595-620, 1977.
    • (1977) Ann. Statist. , vol.5 , pp. 595-620
    • Stone, C.J.1
  • 38
    • 0000439527 scopus 로고
    • Optimal global rates of convergence for nonparametric regression
    • —, “Optimal global rates of convergence for nonparametric regression,” Ann. Statist., vol. 10, pp. 1040-1053, 1982.
    • (1982) Ann. Statist. , vol.10 , pp. 1040-1053


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.