Although, both the problems rely on don't-care bit filling, most of the existing works have considered them separately.
Since each breakpoint region B i is a convex polygon (trapezoid or rectangle), the solution to the above problem relies on computing intersections of convex polygons, a well-known problem in computational geometry (Preparata and Shamos, 1985).
Successfully solving the inverse problem relies on solving the forward problem correctly, because the two problems share one component light propagation in the medium.
This superfluity makes for an odd reading of the text; and the oddness is highlighted by the observation that this first formulation of the sceptical problem relies on Kripke's assumption that we have some idea of what a fact is, independent of a statement's being true.
Therefore, if the considered problem relies on rules with high uncertainty, fuzzy logic is recommended.
The inverse problem relies on a classic local optimization approach implemented with a gradient method.
The foregoing version of the sensory projections problem relies on what was noted to be the unrealistic assumption that the dog- X" law is medog- X by a singlawvisual sensory projection.
It is known that the nonlinear optimal control problem relies on the solution of the Hamilton Jacobi Bellman (HJB) equation, which is a nonlinear partial differential equation that is generally impossible to be solved analytically.
It is well known that the nonlinear optimal control problem relies on the solution of Hamilton Jacobi Bellman (HJB) equation, which is a nonlinear partial differential equation and impossible to be solved analytically.
The applications of the finite element method to the dynamic problem rely on the use of a variation principle to derive the necessary element property's equations.
