Showing papers by "Hüseyin Akçay published in 1994"
14 Dec 1994
TL;DR: In this paper, a non-iterative algorithm for identifying linear time-invariant discrete time state-space models from frequency response data is presented, which recovers the true system of order n if n+2 noise-free frequency response measurements are given at uniformly spaced frequencies.
Abstract: In this paper we present a novel noniterative algorithm for identifying linear time-invariant discrete time state-space models from frequency response data. The algorithm recover the true system of order n if n+2 noise-free frequency response measurements are given at uniformly spaced frequencies. The algorithm is demonstrated to be related to the recent time-domain subspace identification algorithms formulated in the frequency domain. The algorithm is applied to real frequency data, originating from a flexible mechanical structure, with promising results. >
27 citations
14 Dec 1994
TL;DR: In this article, a noniterative linear time-invariant discrete time state-space model from frequency response data is presented and analyzed for identifying linear time invariant discrete-time models.
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 /spl epsiv/ the identification error will be upper bounded by /spl epsiv/ 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. >
25 citations
TL;DR: 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, and shows that the least-square estimate of the transfer function diverges as the order of the FIR system is increased.
19 citations
TL;DR: 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, implying that with a worst- case formulation, the model complexity should not increase indefinitely as the size of the data set increases.
12 citations
TL;DR: In this paper, the authors discuss smooth and sensitive norms for prediction error system identification when the disturbances are magnitude bounded and give formal conditions for sensitive norms, which give an orde...
12 citations
TL;DR: In this article, the problem of system identification in ℋ∞ is investigated in the case when the given frequency response data are not necessarily on a uniformly spaced grid of frequencies.
Abstract: In this paper, the problem of ‘system identification in ℋ∞’ is investigated in the case when the given frequency response data are not necessarily on a uniformly spaced grid of frequencies. A large class of robustly convergent identification algorithms is derived. A particular algorithm is further examined and explicit worst case error bounds (in the ℋ∞ norm) are derived for both discrete-time and continuous-time systems. An example is provided to illustrate the application of the algorithms.
11 citations
01 Jan 1994
TL;DR: A novel non-iterative algorithm to identify linear time-invariant systems from frequency response data and it is shown that the algorithm is consistent under weak conditions on the measurement noise.
Abstract: In this paper, we present a novel non-iterative algorithm to identify linear time-invariant systems from frequency response data. The algorithm is related to the recent time-domain subspace identification techniques. Promising results are obtained when the algorithm is applied to the real frequency data originating from a large flexible structure. A robustness analysis is performed and under weak conditions on the measurement noise, it is shown that the algorithm is consistent.
3 citations