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Träfflista för sökning "WFRF:(Akçay Hüseyin) "

Sökning: WFRF:(Akçay Hüseyin)

  • Resultat 1-10 av 16
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1.
  • Akçay, Hüseyin, et al. (författare)
  • On the Choice of Norms in System Identification
  • 1994
  • Rapport (övrigt vetenskapligt)abstract
    • In this paper we discuss smooth and sensitive norms for prediction error system identification when the disturbances are magnitude bounded. Formal conditions for sensitive norms, which give an order of magnitude faster convergence of the parameter estimate variance, are developed. However, it also is shown that the parameter estimate variance convergence rate of sensitive norms is arbitrarily bad for certain distributions. A necessary condition for a norm to be statistically robust with respect to the family F(C) of distributions with support [-C, C] for some arbitrary C>0 is that its second derivative does not vanish on the support. A direct consequence of this observation is that the quadratic norm is statistically robust among all lp-norms, p⩽2<∞ for F(C).
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2.
  • Akçay, Hüseyin, et al. (författare)
  • On the Choice of Norms in System Identification
  • 1996
  • Rapport (övrigt vetenskapligt)abstract
    • In this paper we discuss smooth and sensitive norms for prediction error system identification when the disturbances are magnitude bounded. Formal conditions for sensitive norms, which give an order of magnitude faster convergence of the parameter estimate variance, are developed. However, it also is shown that the parameter estimate variance convergence rate of sensitive norms is arbitrarily bad for certain distributions. A necessary condition for a norm to be statistically robust with respect to the family F(C) of distributions with support [-C, C] for some arbitrary C>0 is that its second derivative does not vanish on the support. A direct consequence of this observation is that the quadratic norm is statistically robust among all lp-norms, p⩽2<∞ for F(C).
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3.
  • Akçay, Hüseyin, et al. (författare)
  • On the Choice of Norms in System Identification
  • 1994
  • Ingår i: Proceedings of the 10th IFAC Symposium on System Identification. - 9780080422251 ; s. 103-108
  • Konferensbidrag (refereegranskat)abstract
    • In this paper we discuss smooth and sensitive norms for prediction error system identification when the disturbances are magnitude bounded. Formal conditions for sensitive norms, which give an order of magnitude faster convergence of the parameter estimate variance, are developed. However, it also is shown that the parameter estimate variance convergence rate of sensitive norms is arbitrarily bad for certain distributions. A necessary condition for a norm to be statistically robust with respect to the family F(C) of distributions with support [-C, C] for some arbitrary C>0 is that its second derivative does not vanish on the support. A direct consequence of this observation is that the quadratic norm is statistically robust among all lp-norms, p⩽2<∞ for F(C).
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4.
  • Akçay, Hüseyin, et al. (författare)
  • The Least-Squares Identification of FIR Systems Subject to Worst-Case Noise
  • 1993
  • Rapport (övrigt vetenskapligt)abstract
    • The least-squares identification of FIR systems is analyzed assuming that the noise is a bounded signal and the input signal is a pseudo-random binary sequence. A lower bound on the worst-case transfer function error shows that the lest-square estimate of the transfer function diverges as the order of the FIR system is increased. This implies that, in the presence of the worst-case noise, the trade-off between the estimation error due to the disturbance and the bias error (due to unmodeled dynamics) is significantly different from the corresponding trade-off in the random error case: with a worst-case formulation, the model complexity should not increase indefinitely as the size of the data set increases.
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5.
  • Akçay, Hüseyin, et al. (författare)
  • The Least-Squares Identification of FIR Systems Subject to Worst-Case Noise
  • 1994
  • Rapport (övrigt vetenskapligt)abstract
    • The least-squares identification of FIR systems is analyzed assuming that the noise is a bounded signal and the input signal is a pseudo-random binary sequence. A lower bound on the worst-case transfer function error shows that the least-square estimate of the transfer function diverges as the order of the FIR system is increased. This implies that, in the presence of the worst-case noise, the trade-off between the estimation error due to the disturbance and the bias error (due to unmodeled dynamics) is significantly different from the corresponding trade-off in the random error case: with a worst-case formulation, the model complexity should not increase indefinitely as the size of the data set increases.
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6.
  • Akçay, Hüseyin, et al. (författare)
  • The Least-Squares Identification of FIR Systems Subject to Worst-Case Noise
  • 1994
  • Ingår i: Systems & control letters (Print). - 0167-6911. ; 23:5, s. 329-338
  • Tidskriftsartikel (refereegranskat)abstract
    • The least-squares identification of FIR systems is analyzed assuming that the noise is a bounded signal and the input signal is a pseudo-random binary sequence. A lower bound on the worst-case transfer function error shows that the least-square estimate of the transfer function diverges as the order of the FIR system is increased. This implies that, in the presence of the worst-case noise, the trade-off between the estimation error due to the disturbance and the bias error (due to unmodeled dynamics) is significantly different from the corresponding trade-off in the random error case: with a worst-case formulation, the model complexity should not increase indefinitely as the size of the data set increases.
  •  
7.
  • Akçay, Hüseyin, et al. (författare)
  • The Least-Squares Identification of FIR Systems Subject to Worst-Case Noise
  • 1994
  • Ingår i: Proceedings of the 10th IFAC Symposium on System Identification. - 978-0080422251 ; s. 85-90
  • Konferensbidrag (refereegranskat)abstract
    • The least-squares identification of FIR systems is analyzed assuming that the noise is a bounded signal and the input signal is a pseudo-random binary sequence. A lower bound on the worst-case transfer function error shows that the lest-square estimate of the transfer function diverges as the order of the FIR system is increased. This implies that, in the presence of the worst-case noise, the trade-off between the estimation error due to the disturbance and the bias error (due to unmodeled dynamics) is significantly different from the corresponding trade-off in the random error case: with a worst-case formulation, the model complexity should not increase indefinitely as the size of the data set increases.
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8.
  • Ljung, Lennart, 1946-, et al. (författare)
  • Subspace-based Identification of Infinite-dimensional Multivariable Systems from Frequency-response Data
  • 1996
  • Ingår i: Automatica. - Elsevier. - 0005-1098. ; 32:6, s. 885-902
  • Tidskriftsartikel (refereegranskat)abstract
    • A new identification algorithm which identifies low complexity models of infinite-dimensional systems from equidistant frequency-response data is presented. The new algorithm is a combination of the Fourier transform technique with the recent subspace techniques. Given noisefree data, finite-dimensional systems are exactly retrieved by the algorithm. When noise is present, it is shown that identified models strongly converge to the balanced truncation of the identified system if the measurement errors are covariance bounded. Several conditions are derived on consistency, illustrating the trade-offs in the selection of certain parameters of the algorithm. Two examples are presented which clearly illustrate the good performance of the algorithm.
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9.
  • McKelvey, Tomas, et al. (författare)
  • An Efficient Frequency Domain State-Space Identification Algorithm : Robustness and Stochastic Analysis
  • 1994
  • Ingår i: Proceedings of the 33rd IEEE Conference on Decision and Control. - 0-7803-1968-0 ; s. 3348-3353 vol.4
  • Konferensbidrag (refereegranskat)abstract
    • In this paper we present and analyze a novel algorithm for identifying linear time-invariant discrete time state-space models from frequency response data. The algorithm is noniterative and exactly recovers a true system of order n, if n+2 noise-free uniformly spaced frequency response measurements are given. Analysis show that if the measurements are perturbed with errors upper bounded by ε the identification error will be upper bounded by ε and hence the algorithm is robust. An asymptotic stochastic analysis show, under weak assumptions, that the algorithm is consistent if the measurements are contaminated with noise.
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10.
  • McKelvey, Tomas, et al. (författare)
  • An Efficient Frequency Domain State-Space Identification Algorithm Robustness and Stochastic Analyis
  • 1994
  • Rapport (övrigt vetenskapligt)abstract
    • In this paper we present and analyze a novel algorithm for identifying linear time-invariant discrete time state-space models from frequency response data. The algorithm is noniterative and exactly recovers a true system of order n, if n+2 noise-free uniformly spaced frequency response measurements are given. Analysis show that if the measurements are perturbed with errors upper bounded by ε the identification error will be upper bounded by ε and hence the algorithm is robust. An asymptotic stochastic analysis show, under weak assumptions, that the algorithm is consistent if the measurements are contaminated with noise.
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  • Resultat 1-10 av 16
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konferensbidrag (5)
tidskriftsartikel (4)
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refereegranskat (9)
övrigt vetenskapligt (7)
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Akçay, Hüseyin, (16)
McKelvey, Tomas, (8)
Ljung, Lennart, 1946 ... (8)
Hjalmarsson, Håkan, (6)
Hjalmarsson, Håkan, ... (1)
McKelvey, Tomas, 196 ... (1)
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