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Any of a set of objects resulting from the generalization of a two-dimensional circle and a three-dimensional sphere to n dimensions. In n-dimensional space, a hypersphere is the set of all points that are a given distance, called the radius, from a given point, called the center.
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V-detector algorithm generates candidate detectors randomly, in which the radius of a detector is dynamically resized until the boundary of the region comes in contact with the nearest hypersphere of a self element.
After the initialization, every parameter [[theta].sub.i] will search within a small hypersphere to find one parameter with the smallest error as its target [T.sub.i].
As illustrated in Figure 2, a XNN hypersphere, formed by the K's nearest neighboring (XNN) points of [X.sub.i], is a cloud composed of Km-dimensional neighboring points around [X.sub.i].
Bao, "Radar HRRP statistical recognition based on hypersphere model," Signal Processing, vol.
Whereas in a bulk universe, the event horizon of a four dimensional black hole would have to be three dimensional, known as a "hypersphere".
A lot of discriminators which are suitable for corresponding features have been designed just like the generalized ratio test (GLRT) detector [9] and the hypersphere support vector machine (HS-SVM) [16].
One of the most noteworthy depth-first techniques is the SE algorithm [26, 27], which restricts the search for the perturbation vector to the set of nodes with [D.sub.i][less than or equal to] R that lie within a hypersphere of radius R centered around a reference signal.
Thus, at each iteration, the search for an optimum new point in the w space is restricted to a small hypersphere centered at the point defined by the current vector w.
The fuzzy retractions of [[??].sup.4] model are the fuzzy unit hyperboloid, fuzzy hyperbolic, fuzzy hypersphere, fuzzy circle, and fuzzy minimal manifolds.
is a map which carries M to the unit hypersphere [S.sup.n] of [E.sup.n+1.] The Gauss map is a continuous map such that v(p)is a unitnormal vector [zeta](p) of M at p.
Another way to calculate dfactor depends on estimate the volume of the unit; assume the shape to be circle, sphere, or hypersphere and we estimate the volume of the shape with radius equal max or avg; then divide the volume by the NumCore as indication to the unit density.