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In this control scheme an explicit control Lyapunov function (CLF) and the strict assumption of linearly parametric uncertainties made on system structures can be avoided.
Because traditional phylogenetic methods used in biogeographic reconstruction are based on trees rather than networks, they follow the strict assumption in which dispersal among geographical units have occurred on the basis of single dispersal routes across regions and are, therefore, incapable of modelling multiple alternative dispersal scenarios.
This is a strict assumption, since subcarriers which are less than apart are not fully correlated.
First, the multinomial probit model, unlike its logit counterpart, allows the relaxation of the strict assumption of independence of irrelevant alternatives (IIA).
The strict assumption that information flows along shortest paths (geodesics) is not always appropriate, as discussed, by Newman [18], who proposes a random walk betweenness measure computed using matrix methods.
The only strict assumption made is that of a monotonic odor response function.
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Other methods are robust but place prohibitively strict assumptions on the input, e.g., no hollow cavities, non-manifold edges or self-intersections.
The classical Warburg model has very strict assumptions, however, it is used in a wide range of scenarios where assumptions may not be satisfied.
Prior works on network resource optimization always transformed the nonconvex optimization problem into a convex one under some strict assumptions, which do not meet the actual networks.
As the corresponding dynamic moment equations can only be computed in a closed form under strict assumptions, approximate moment methods have to be applied.
We focus on a general, data-driven optimization-based method for generating scenario trees that does not require strict assumptions on the probability distributions of the uncertain parameters.
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