Network robust

Dong M (2006) Development of supply chain network robustness index. Int J Serv Oper Info 1... 

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]. 

SONNIA - KSOM and CPG neural networks. The key features are robustness of training and excellent visualization capabilities. 

Internal Model Control was diseussed in relation to robust eontrol in seetion 9.6.3 and Figure 9.19. The IMC strueture is also applieable to neural network eontrol. The plant model GmC) in Figure 9.19 is replaeed by a neural network model and the eontroller C(.v) by an inverse neural network plant model as shown in Figure 10.30. 

Even so, artificial neural networks exhibit many brainlike characteristics. For example, during training, neural networks may construct an internal mapping/ model of an external system. Thus, they are assumed to make sense of the problems that they are presented. As with any construction of a robust internal model, the external system presented to the network must contain meaningful information. In general the following anthropomorphic perspectives can be maintained while preparing the data ... 

Just prior to Rubin s publication, another article appeared focusing on substructures of graphdiyne [63]. Like the other researchers in the PDM area, the Haley team was intrigued by the predictions of useful materials properties and technological applications for this and similar carbon-rich systems [5c, 50,52]. In particular, topochemical polymerization of a crystalline substructure of this network could produce an environmentally robust material with a large third-... 

Winkler DA. Neural networks as robust tools in drug lead discovery and development. Mol Biotechnol 2004 27 139-68. 

A way to obtain an idea on the robustness of the obtained solution is to retrain the network with a different weight initialization. The results of the different training sessions can be used to define a range around the performance curve as shown in Fig. 44.17. This procedure can also be used to compare different networks [20]. 

B. Walczak, Neural networks with robust backpropagation learning algorithm. Anal. Chim. Acta, 322 (1996) 21-30. 

L. Kiernan, J.D. Mason and K. Warwick, Robust initialisation of Gaussian radial basis function networks using partitioned k-means clustering. Electron. Lett., 32 (1996) 671-672. 

Kavuri, S. N., and Venkatasubramanian, V., Using fuzzy clustering with ellipsoidal units in neural networks for robust fault classification, Comput. Chem. Eng. 17(8), 765 (1993). 

Based on this physical view of the reaction dynamics, a very broad class of models can be constructed that yield qualitatively similar oscillations of the reaction probabilities. As shown in Fig. 40(b), a model based on Eckart barriers and constant non-adiabatic coupling to mimic H + D2, yields out-of-phase oscillations in Pr(0,0 — 0,j E) analogous to those observed in the full quantum scattering calculation. Note, however, that if the recoupling in the exit-channel is omitted (as shown in Fig. 40(b) with dashed lines) then oscillations disappear and Pr exhibits simple steps at the QBS energies. As the occurrence of the oscillation is quite insensitive to the details of the model, the interference of pathways through the network of QBS seems to provide a robust mechanism for the oscillating reaction probabilities. 

For PyMS to be used for (1) routine identification of microorganisms and (2) in combination with ANNs for quantitative microbiological applications, new spectra must be comparable with those previously collected and held in a data base.127 Recent work within our laboratory has demonstrated that this problem may be overcome by the use of ANNs to correct for instrumental drift. By calibrating with standards common to both data sets, ANN models created using previously collected data gave accurate estimates of determi-nand concentrations, or bacterial identities, from newly acquired spectra.127 In this approach calibration samples were included in each of the two runs, and ANNs were set up in which the inputs were the 150 new calibration masses while the outputs were the 150 old calibration masses. These associative nets could then by used to transform data acquired on that one day to data acquired at an earlier data. For the first time PyMS was used to acquire spectra that were comparable with those previously collected and held in a database. In a further study this neural network transformation procedure was extended to allow comparison between spectra, previously collected on one machine, with spectra later collected on a different machine 129 thus calibration transfer by ANNs was affected. Wilkes and colleagues130 have also used this strategy to compensate for differences in culture conditions to construct robust microbial mass spectral databases. 

The lifetime of the excited state of fluorophores may be altered by physical and biochemical properties of its environment. Fluorescence lifetime imaging microscopy (FLIM) is thus a powerful analytical tool for the quantitative mapping of fluorescent molecules that reports, for instance, on local ion concentration, pH, and viscosity, the fluorescence lifetime of a donor fluorophore, Forster resonance energy transfer can be also imaged by FLIM. This provides a robust method for mapping protein-protein interactions and for probing the complexity of molecular interaction networks. 

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). 

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