In this regard, preference degree of fuzzy numbers and the concept of fuzzy distance can be employed.
A membership value between 0 and 1 indicate the degree of fuzzy relationship.
Two parameters are set: deterministic factor is used to express confidence degree of fuzzy rules while reliability value is used to express the reliability degree of storing proposition in decision information place.
On the basis of Delphi, analytic hierarchy process, grey incidence degree and fuzzy system theory, a grey synthetical evaluation model is put forward to quantitatively assess university's engineering innovation ability.
In this section, we present the details of the RL-FNN algorithm and how it computes the degrees of fuzzy membership functions and does the inference for our simulation scenario.
The operation of the fuzzification calculates the degrees for each evaluated parameter (input) belonging to the three membership functions, e.g., for RC_S this operation calculates {(upmu _{N})(RC_S), (upmu _{M} RC_S), (upmu _{F})(RC_S),} with (upmu _{N})(RC_S), (upmu _{M} RC_S) and (upmu _{F})(RC_S) the membership degrees of fuzzy sets N, M, and F, respectively.
Fuzzy subsethood of A in B is the degree to which fuzzy set as point A belongs to fuzzy set as point B. The fuzzy measure of comparison F Comp ( A, B ) = F Sim ( A,B ) – (K-1), where K is the fuzzy measure of symmetry breaking of conditions previously defined by us from the fuzzy subsethood theorem and measured in fuzzy cardinality.
It is a measure of the degree to which fuzzy set A is similar to fuzzy set B when different conditions are taken into account and removed from the comparison.
The output of the fuzzification process demonstrates a fuzzy degree of membership between 0 and 1.
The crisp inputs are taken from these variables, and these inputs are given a degree to appropriate fuzzy sets.
