In this paper experiment design for identification of ill-conditioned systems is studied.
The objective of this paper is to develop an order estimation algorithm for model identification of ill-conditioned processes using subspace methods.
A comparison of the effectiveness between five different methods to generate excitation input signals for the identification of ill-conditioned processes is presented.
In this paper open-loop (uncorrelated and rotated) random signals are compared against inputs generated in closed-loop operation, with the aim of finding the most appropriate ones to be used in multivariable subspace identification of ill-conditioned processes.
In this paper open-loop (uncorrelated and rotated) random signals are compared against closed-loop signals with the aim of finding the most appropriate ones to be used in multivariable subspace identification of ill-conditioned systems.
The primary purpose of this paper was to develop and evaluate a physiologically based system for the identification of acutely ill children in hospital environments.
Unfortunately, various barriers to this desired continuum of care exist, including the lack of resources needed in jails to perform accurate identification of mentally ill offenders, and to provide sufficient clinical services to the offender both while incarcerated, and again in the community after release.
Early identification of critically ill surgical patients who are not fully resuscitated improves outcome.
Many groups have advocated identification of critically ill medical patients by abnormal or deteriorating physiological parameters.
In this context, the study by Posteraro and colleagues [ 1] is an important step forward on the way to early identification of critically ill patients with invasive candidiasis.
