Sentence examples for plausibility functions from inspiring English sources
It shows that some kinds of covering approximation operators can be characterized by belief and plausibility functions, while others do not share this property.
Third, we develop several measurements by using the belief and plausibility functions, and characterize knowledge reduction of multigranulation spaces based on these measurements.
We firstly employ belief and plausibility functions to measure lower and upper approximations in neighborhood-covering rough sets, and then, the attribute reductions of covering information systems and decision systems are characterized by these respective functions.
First, we introduce a type-1 belief structure for a multigranulation space, and prove that a pair of lower and upper pessimistic multigranulation approximations correspond to a pair of belief and plausibility functions under this belief structure.
At second phase, the acquired results of previous stage are assumed as basic beliefs for the problem propositions and in this way, the belief and plausibility functions (or the belief interval) are set.
The sufficient conditions under which covering approximation operators can be measured by belief and plausibility functions are further explored, and the corresponding counterexamples are given to illustrate those that cannot be measured.
The lower (upper) uncertainty boundary of each well-log is calculated by belief (plausibility) function.
Moreover, the following relationship is true for the belief function and the plausibility function under all circumstances.
The lower bound of this domain is determined by the belief function and the higher bound by the plausibility function.
Besides, in Dempster-Shafer theory, the belief function Bel and the plausibility function Pl are defined as Bel B = ∑ A k | A k ⊆ B m A k (12) Pl B = ∑ A k | A k ∩ B ≠ ∅ m A k (13).
Similarly, the plausibility function for cell c i is, from (4) pl i { A } = 1 − κ · m i ( B ) (14a) pl i { B } = 1 − κ · m i ( A ) (14b).
