Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 3

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: robust identification

Sortuj według:

Ogranicz wyniki do:

Robust Identification Approach to Individualized Anemia Model

Akabua E., Inanc T., Gaweda A., Brier M. E., Kim S., Zurada J. M.

Journal of Applied Computer Science Methods

2011

Vol. 3 No. 2

65--75

Over ninety percent of End Stage Renal Disease (ESRD) patients suffer from anemia due to insufficient endogenous production of human erythropoietin. Until the advent of Recombinant Human Erythropoietin (r-HuEPO) over 30 years ago, patients with ESRD were treated mainly with multiple blood transfusions. The high cost of r-HuEPO in addition to the narrow margin between an effective do-sage and toxicity in drug administration calls for optimal dosage strategy capable of minimizing cost and toxicity while at the same time achieving the desired do-sage outcome. It is well known from control theory that a controller can be de-signed for any plant provided there is readily available a valid model for such a plant. We present Robust Identification procedure, a dimensionality reduction technique capable of capturing the inherent dynamics of anemia patients; conse-quently producing individualized model suitable for robust control synthesis and any other controller design methodologies.

Robust linear predictor as EEG fluctuation analyzer in diagnosis of Alzheimer's disease

Zachová D., Kukal J., Vyšata O.

Archives of Control Sciences

2011

Vol. 21, no. 2

121-133

The paper is oriented to EEG signal analysis, which is focused to quasi-stationarity hypothesis that the statistical properties of the channel signal fluctuate in time. Robust linear predictor is used for short segments of EEG as low-pass filter and the difference between the raw EEG and filter output was subject of statistical testing. Novelty is in the fluctuation measurement which enables to classify the Alzheimer's disease patients against controls.

Robust identification of an augmented Gurson Model for elasto-plastic porous media

Nowak Z., Stachurski A.

Archives of Mechanics

2006

Vol. 58, nr 2

125-154

In the paper we investigate the robust identification approach to identify the material parameters in the augmented Gurson model for the elasto-plastic porous media. We consider the robust loss function given by Huber [9], Beaton and Tuckey [38] and the loss function based on the l1-norm. The resulting minimization problem is solved by means of our own implementation of the Boender et al. global minimization method. Our aim is to compare the results with our earlier standard least squares estimates. In the paper, the effects of nucleation and growth of voids in the plastic porous media are investigated. Three different forms of the model are considered: the augmented Gurson model (total porosity model) with variable nucleation and growth material function, the same model with constant growth material function and the separated porosity model. The identification of the material functions parameters is based on Fischer's experimental data set for axisymmetric tension of steel specimens.