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We use two metrics to evaluate how well a graph approximates a set of isolated cliques: the mean clustering coefficient, C, which reflects local transitivity, and the graph component density, D, a measure of global transitivity.
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The result of this methodology is the creation of a chain of rigid bodies connected by revolute and prismatic joints that can approximate a set of design profiles.
It is well known that FS are able to approximate a set of piecewise functions as a single-rule function; the accuracy depends on the number of terms and satisfaction of Dirichlet's theorem.
Experiments on three flood prediction problems showed that both algorithms approximate a set of 500 failure probabilities of 10−3 10−2 or derived 95% epistemic quantiles with a total of only 500 1000 function evaluations, outperforming importance sampling, iterative FORM and regression splines metamodels.
In the experimental section, we demonstrate the advantage of mSVDD: although learning in the input space, the set of hyperspheres offered by mSVDD is able to approximate a set of contours formed by a single optimal hypersphere in the feature space.
By the fuzzy approximation, the HJI in (12) can be approximated by a set of algebraic inequalities in (19).
The optimized shape is then approximated by a set of developable patches.
The Hessian matrix is approximated, using a set of box-type filters.
Original system dynamics is approximated by a set of local NARX models combined by a special mixing rule.
The surface is approximated by a set of line segments fitted at the boundary of every two neighboring computational cells.
The chamber section of the soft gripper is approximated as a set of active joints, which are represented by circles.
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