Adaptation robustness

Wu, X., and Cinar, A. (1996). An adaptive robust M-estimator for nonparametric nonlinear system identification. J. Proc. Control 6, 233-239. 

Adaptive, robust single-step tests of size a, both individual and simultaneous, can be based on the corresponding confidence intervals already discussed. To test the hypothesis... 

Better yet, one can obtain adaptive robust tests that are more easily implemented and are still of the specified size. For testing each null hypothesis //o, Pi = 0, one need not exclude the corresponding estimator ft from the computation of the denominator or standard error, because Pi = 0 under the null hypothesis. 

There are important advantages in using adaptive robust procedures that strongly control the error rate. Strong control of the error rate provides the statistical rigor for assessing the believability of any assertions made about the significance of the main effects or interactions, whether confidence intervals or tests are applied. Use of a robust adaptive procedure allows the data to be used efficiently. 

Yao B, Xu L (2002) Adaptive robust motion control of linear motors for precision manufacturing. Mechatrorrics 12(4) 595-616... 

Neuronal networks are nowadays predominantly applied in classification tasks. Here, three kind of networks are tested First the backpropagation network is used, due to the fact that it is the most robust and common network. The other two networks which are considered within this study have special adapted architectures for classification tasks. The Learning Vector Quantization (LVQ) Network consists of a neuronal structure that represents the LVQ learning strategy. The Fuzzy Adaptive Resonance Theory (Fuzzy-ART) network is a sophisticated network with a very complex structure but a high performance on classification tasks. Overviews on this extensive subject are given in [2] and [6]. 

Electroosmotic flow (EOE) is thus the mechanism by which liquids are moved from one end of the sepai ation capillai y to the other, obviating the need for mechanical pumps and valves. This makes this technique very amenable to miniaturization, as it is fai simpler to make an electrical contact to a chip via a wire immersed in a reservoir than to make a robust connection to a pump. More important, however, is that all the basic fluidic manipulations that a chemist requires for microchip electrophoresis, or any other liquid handling for that matter, have been adapted to electrokinetic microfluidic chips. 

The %HIA, on a scale between 0 and 100%, for the same dataset was modeled by Deconinck et al. with multivariate adaptive regression splines (MARS) and a derived method two-step MARS (TMARS) [38]. Among other Dragon descriptors, the TMARS model included the Tig E-state topological parameter [25], and MARS included the maximal E-state negative variation. The average prediction error, which is 15.4% for MARS and 20.03% for TMARS, shows that the MARS model is more robust in modeling %H1A. 

In the previous section, the adaptation of the RIS model was based on the distance between next-nearest neighbor beads. This approach is obviously inadequate for CH3-CHX-CH2-CHX-CH3, because it necessarily abandons the ability to attribute different conformational characteristics to the meso and racemo stereoisomers. Therefore a more robust adaption of the RIS model to the 2nnd lattice is necessary if one wants to investigate the influence of stereochemical composition and stereochemical sequence on vinyl polymers [156]. Here we describe a method that has this capability. Of course, this method retains the ability to treat chains such as PE in which the bonds are subject to symmetric torsion potential energy functions. 

First, the use of higher dimensional robust networks (such as the two-dimensional GS network) simplifies crystal engineering because is reduces crystal design to the last remaining dimension. The use of two-dimensional supramolecular modules, in particular, provides an easily conceptualized mechanism for topological adaptation (in the case of GS networks the arrangement of the pillars). 

Butler, R., McDonald, J., Nelson, R., and White, S. (1990). Robust and partially adaptive estimation of regression models. Rev. Ecoti. Stat. 72, 321. 

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