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Two newly formed uncertainty analysis methods, including the Kriging-based Monte Carlo simulation (KMCS) and Kriging-based Taylor series approximation (KTSA), are carried out using a global approximation Kriging modeling method.
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The adequacy of the model was explored using a global index that is responsive to sample size and complexity: the root mean square error of approximation (RMSEA) [ 16].
Note that RMA uses a linear intensity approximation which ignores saturation at high transcript concentrations at one hand-side and corrects the intensities for non-specific hybridization using a global background level on the other hand-side.
The first method is based on a local approximation using radial basis functions, whereas the second method uses two-dimensional Chebyshev polynomials in order to yield a global approximation of the structural grid deformations.
Due to the use of the fundamental solution as basis functions, the method leads to a global approximation scheme in both spatial and time domains.
I use a global palette.
Back-propagation neural networks (BPN) have been extensively used as global approximation tools in the context of approximate optimization.
Artificial neural networks (ANN) have been extensively used as global approximation tools in the context of approximate optimization.
However, before proceeding to improved, regional metamodelling, the dominating relationships and patterns between the 9 input parameters and the 16 output state trajectories of the mammalian circadian clock model were assessed using the global approximations.
Nonlinear multi-variance behaviors are transformed to a multi-local linear model using a fuzzy approximation method, which is used to implement control steadily and rapidly for the global system.
The graph is obtained using a convex approximation described here.
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