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Strong Robustness (strong + robustness)

Distribution by Scientific Domains
Engineering60%
Life Sciences40%

Selected Abstracts

Strong robustness in multi-phase adaptive control: the basic scheme

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2004
Maria Cadic
Abstract The general structure of adaptive control systems based on strong robustness is introduced. This approach splits into two phases. In the first phase, emphasis is put on identification until enough information is obtained in order to design a controller that stabilizes the actual system, and even under adaptation. This is achieved if the input sequence is computed in such a way that the uncertainty on the parameters of the system to be controlled becomes sufficiently small. Then, in the second phase, effort is shifted to control via a traditional certainty equivalence type of strategy. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Rapid categorization of achromatic natural scenes: how robust at very low contrasts?

EUROPEAN JOURNAL OF NEUROSCIENCE, Issue 7 2005
Marc J.-M.
Abstract The human visual system is remarkably good at categorizing objects even in challenging visual conditions. Here we specifically assessed the robustness of the visual system in the face of large contrast variations in a high-level categorization task using natural images. Human subjects performed a go/no-go animal/nonanimal categorization task with briefly flashed grey level images. Performance was analysed for a large range of contrast conditions randomly presented to the subjects and varying from normal to 3% of initial contrast. Accuracy was very robust and subjects were performing well above chance level (, 70% correct) with only 10,12% of initial contrast. Accuracy decreased with contrast reduction but reached chance level only in the most extreme condition (3% of initial contrast). Conversely, the maximal increase in mean reaction time was ,,60 ms (at 8% of initial contrast); it then remained stable with further contrast reductions. Associated ERPs recorded on correct target and distractor trials showed a clear differential effect whose amplitude and peak latency were correlated respectively with task accuracy and mean reaction times. These data show the strong robustness of the visual system in object categorization at very low contrast. They suggest that magnocellular information could play a role in ventral stream visual functions such as object recognition. Performance may rely on early object representations which lack the details provided subsequently by the parvocellular system but contain enough information to reach decision in the categorization task. [source]

Power and robustness of a score test for linkage analysis of quantitative traits using identity by descent data on sib pairs

GENETIC EPIDEMIOLOGY, Issue 4 2001
Darlene R. Goldstein
Abstract Identification of genes involved in complex traits by traditional (lod score) linkage analysis is difficult due to many complicating factors. An unfortunate drawback of non-parametric procedures in general, though, is their low power to detect genetic effects. Recently, Dudoit and Speed [2000] proposed using a (likelihood-based) score test for detecting linkage with IBD data on sib pairs. This method uses the likelihood for ,, the recombination fraction between a trait locus and a marker locus, conditional on the phenotypes of the two sibs to test the null hypothesis of no linkage (, = ½). Although a genetic model must be specified, the approach offers several advantages. This paper presents results of simulation studies characterizing the power and robustness properties of this score test for linkage, and compares the power of the test to the Haseman-Elston and modified Haseman-Elston tests. The score test is seen to have impressively high power across a broad range of true and assumed models, particularly under multiple ascertainment. Assuming an additive model with a moderate allele frequency, in the range of p = 0.2 to 0.5, along with heritability H = 0.3 and a moderate residual correlation , = 0.2 resulted in a very good overall performance across a wide range of trait-generating models. Generally, our results indicate that this score test for linkage offers a high degree of protection against wrong assumptions due to its strong robustness when used with the recommended additive model. Genet. Epidemiol. 20:415,431, 2001. © 2001 Wiley-Liss, Inc. [source]

Strong robustness in multi-phase adaptive control: the basic scheme

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2004
Maria Cadic
Abstract The general structure of adaptive control systems based on strong robustness is introduced. This approach splits into two phases. In the first phase, emphasis is put on identification until enough information is obtained in order to design a controller that stabilizes the actual system, and even under adaptation. This is achieved if the input sequence is computed in such a way that the uncertainty on the parameters of the system to be controlled becomes sufficiently small. Then, in the second phase, effort is shifted to control via a traditional certainty equivalence type of strategy. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Robust Neural Network Controller Design For A Biaxial Servo System

ASIAN JOURNAL OF CONTROL, Issue 4 2007
Chih-Hsien Yu
ABSTRACT A robust control method for synchronizing a biaxial servo system motion is proposed in this paper. A new neural network based cross-coupled control and neural network techniques are used together to cancel out the skew error. In the proposed control scheme, the conventional fixed gain PID cross-coupled controller (PIDCCC) is replaced with the neural network cross-coupled controller (NNCCC) to maintain biaxial servo system synchronization motion. In addition, neural network PID position velocity and velocity controllers provide the necessary control actions to maintain synchronization while following a variable command trajectory. This scheme provides strong robustness with respect to uncertain dynamics and nonlinearities. The simulation results reveal that the proposed control structure adapts to a wide range of operating conditions and provides promising results under parameter variations and load changes. [source]