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Volumn 34, Issue 1, 2003, Pages 59-70

On unbiased parameter estimation of linear systems using noisy measurements

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Indexed keywords


EID: 85011526505     PISSN: 01969722     EISSN: 10876553     Source Type: Journal    
DOI: 10.1080/01969720302851     Document Type: Article
Times cited : (14)

References (12)
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    • Stochastic analysis of gradient adaptive identification of nonlinear systems with memory for Gaussian data and noisy input and output measurements
    • Bershad, N. J., P. Celka, and J. M. Vesin. 1999. Stochastic analysis of gradient adaptive identification of nonlinear systems with memory for Gaussian data and noisy input and output measurements. IEEE Transactions on Signal Processing, 47:675-689.
    • (1999) IEEE Transactions on Signal Processing , vol.47 , pp. 675-689
    • Bershad, N.J.1    Celka, P.2    Vesin, J.M.3
  • 4
    • 0021853670 scopus 로고
    • Identification of linear systems with input and output noise: The Koopmans-Levin method. IEE Proceedings
    • Fernando, K. V., and H. Nicholson. 1985. Identification of linear systems with input and output noise: The Koopmans-Levin method. IEE Proceedings- Control Theory and Applications, 132:30-36.
    • (1985) Control Theory and Applications , vol.132 , pp. 30-36
    • Fernando, K.V.1    Nicholson, H.2
  • 5
    • 0003807773 scopus 로고    scopus 로고
    • 3rd ed. Englewood Cliffs, NJ: Prentice-Hall
    • Haykin, S. 1996. Adaptive filter theory, 3rd ed. Englewood Cliffs, NJ: Prentice-Hall.
    • (1996) Adaptive Filter Theory
    • Haykin, S.1
  • 6
    • 79953806801 scopus 로고
    • Suboptimal estimation of the parameters of discrete systems in the presence of correlated noise
    • James, P. N., P. Souter, and D. C. Dixon. 1972. Suboptimal estimation of the parameters of discrete systems in the presence of correlated noise. Electronics Letters, 8:411-412.
    • (1972) Electronics Letters , vol.8 , pp. 411-412
    • James, P.N.1    Souter, P.2    Dixon, D.C.3
  • 8
    • 0019610533 scopus 로고
    • Identification of stochastic linear systems in presence of input noise
    • Soderstrom, T. 1981. Identification of stochastic linear systems in presence of input noise. Automatica, 17:713-725.
    • (1981) Automatica , vol.17 , pp. 713-725
    • Soderstrom, T.1
  • 9
    • 0033750379 scopus 로고    scopus 로고
    • Learning neural networks with noisy inputs using the errors-in-variables approach
    • Van Gorp, J., J. Schoukens, and R. Pintelon. 2000. Learning neural networks with noisy inputs using the errors-in-variables approach. IEEE Transactions on Neural Networks, 11:402-414.
    • (2000) IEEE Transactions on Neural Networks , vol.11 , pp. 402-414
    • Van Gorp, J.1    Schoukens, J.2    Pintelon, R.3
  • 10
    • 0030406142 scopus 로고    scopus 로고
    • Modified least-squares identification of linear systems with noisy input and output observations
    • Kobe, Japan
    • Zheng, W. X. 1996. Modified least-squares identification of linear systems with noisy input and output observations. In Proceedings of the 35th IEEE Conference on Decision and Control, vol. 1, pp. 1067-1068, Kobe, Japan.
    • (1996) Proceedings of the 35Th IEEE Conference on Decision and Control , vol.1 , pp. 1067-1068
    • Zheng, W.X.1
  • 12
    • 0034309414 scopus 로고    scopus 로고
    • Parametric identification of linear noisy input-output systems
    • Zheng, W. X. 2000. Parametric identification of linear noisy input-output systems. Cybernetics and Systems, 31:803-816
    • (2000) Cybernetics and Systems , vol.31 , pp. 803-816
    • Zheng, W.X.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.