Sentence examples for transformation to transform from inspiring English sources
One utilizes the parameterized co-ordinate transformation to transform the original nonlinear system into singularly perturbed model and the composite Lyapunov approach is then applied for output tracking.
Our strategy is to use a damping boundary feedback combined with a backstepping-like coordinate transformation to transform the system into an exponentially stable system.
In this paper, we use the local (C^{2} -conjugate transformation to transform nonlinear stoC^{2} -conjugatetransformationo ordinary differentoal systransformgive some sufficienonlinearionstochasticexponential stability andifferentialy of systemstoc differential systems.
The logit transformation is a standard transformation to transform the probability scale (0,1) to the real axis (-infinity, +infinity) to allow standard regression techniques (including ANOVA) to test the effects of co-variables (mask type, age class, sex, activity, duration of use) on transformed PFs in a linear model, using the statistical application R (version 2.5.0).
For instance, Wolfe et al. [ 16] used the logarithmic transformation to transform a skewed distribution to a distribution that is approximately normal.
The maximum likelihood linear regression (MLLR) method[2] estimates a set of linear transformations to transform an SI model into an SA model.
Instead of estimating the SD model directly, the MLLR method estimates a set of linear transformations to transform an SI model into a new SA model.
After that, we applied the inverse linear and non-linear transformations to transform the ROIs into the individual diffusion space.
Also, wherever possible, XSL transformations (to transform XML data into human readable HTML pages) are performed [ 25].
In the second part, we introduce new transformation rules to transform the extended and marked BPEL to an automata model.
For this, we present a two-stage coordinate transformation method to transform any given time-delay system into an observable canonical form where time delays appear in the input and output vectors, but not in the state vector.
