The membership functions of RC_I, EC_I, and NC_I are defined as shown in Fig. 7d f, where Far (F), Medium (M), and Near (N) are the fuzzy variables.
Then, the membership functions of RC_S, EC_S, and NC_S are defined as shown in Fig. 7a c, where Near (N), Medium (M), and Far (F) are the fuzzy variables.
Those elements are the fuzzy variables, inference rules, and the membership functions.
where R shows the degree of decision making, x i is the fuzzy variable and β(x i ) is its membership function.
where α is the number of contenders, β is the buffer occupancy percentage of parent (next-hop) sensor node and γ is the fuzzy variable term for the traffic load.
As shown in (3), the sum of the stochastic variable and the fuzzy variable is the fuzzy stochastic variable [13], where (P_{{ell.{text{random}}}}) and (P_{{ell.{text{fuzzy}}}}) represent the stochastic and fuzzy portions of the output power, respectively.
To deal with the inherent uncertainty, a transformation of variables into fuzzy variables is done, and through the expected value operation, the fuzzy variables are subsequently transformed into determined ones.
The fuzzy variables are defined in the rule base as {NS (Negative Small), NB (Negative Big), Z (Zero), PS (Positive Small) and PB (Positive Big)}.
The Fuzzy variables are involved in the description of certain situations, phenomenon, or process generally containing fuzzy qualifiers.
In the fuzzy method, the wind power is deemed as the fuzzy variable, and the fuzzy set theory is used to model the corresponding DP issue [17, 18].
