In this paper, the problem of identifying stochastic linear discrete-time systems from noisy input/output data is addressed. The input noise is supposed to be white, while the output noise is assumed to be coloured. Some methods based on instrumental variable techniques are studied and compared to a least squares bias compensation scheme with the help of Monte Carlo simulations.

In this paper, a solution to the frequency-domain system identification of a linear time-invariant system is investigated. A generalization of the total least squares algorithm is shown and analyzed. Some simulation examples on real measured data are given, in order to illustrate the properties of the new method in practice.

A recent iterative procedure by Lu et al. for designing IIR filters is recognized as the Sanathanan–Koerner or Steiglitz–McBride iteration, which was proposed originally in 1963 and 1965, respectively, for system identification purposes. We re-examine some claims and issues related to Lu et al.’s paper1 in view of various properties that have been deduced for the Steiglitz–McBride iteration in ...

Recent studies have demonstrated that correntropy is an efficient tool for analyzing higher-order statistical moments in nonGaussian noise environments. Although it has been used with complex data, some adaptations were then necessary without deriving a generic form so that similarities between complex random variables can be aggregated. This paper presents a novel probabilistic interpretation ...

In this paper, the problem of identifying stochastic linear discrete-time systems from noisy input/output data is addressed. The input noise is supposed to be white, while the output noise is assumed to be coloured. Some methods based on instrumental variable techniques are studied and compared to a least squares bias compensation scheme with the help of Monte Carlo simulations.

Research on organizational identification (OI) has increased dramatically in the various fields during the past decade, but little is known about OI in the area of information systems (IS). This study explores the effect of OI on perceived usefulness and end-user satisfaction as a precedence of IS success at the individual level. A total of 135 useful responses were analyzed by using the partia...

In continuous-time system identification and adaptive control the least-squares parameter estimation algorithm has always been usedwith regressor filtering, which adds to the dynamic order of the identifier and affects its performance. We present an approach for designing a least-squares estimator that uses an unfiltered regressor. We also consider a problem of adaptive nonlinear control and pr...

NORMAL EQUATIONS Brett Ninness Håkan Hjalmarsson Dept. of Elec. & Comp. Eng, Uni. Newcastle, Australia. email:[email protected], FAX: +61 2 49 21 69 93 Dept. Sensors, Signals & Systems, Royal Inst. Technology, S-100 44 Stockholm, Sweden. email:[email protected], FAX: +46 8 790 7329 Abstract: There has been recent interest in using orthonormalised forms of fixed denominator m...

This paper presents a stable on-line adaptive identification technique for the identification of finitedimensional dynamical models of dynamically positioned underwater robotic vehicles. A direct comparison of this method to a conventional, off-line, least-squares method is presented. Using experimental data obtained using the JHU Remotely Operated Vehicle, both methods are employed to develop ...

Abstrart-Many practical applications of control system design based on input-output measurements permit the repeated application of a system identification procedure operating on closed-loop data together with successive refinements of the designed controller. Here we develop a paradigm for such an iterative design. The key to the procedure is to account for evaluated modelling error in the con...