The purpose of this note is to obtain some properties of (∈,∈ ∨q)fuzzy bi-ideals in a Γ-semigroup in order to characterize regular and intra-regular Γ-semigroups. Keywords—Regular Γ-semigroup, belong to or quasi-coincident, (∈,∈ ∨q)-fuzzy subsemigroup, (∈,∈ ∨q)-fuzzy bi-ideals.

In this paper we have studied   q   , -fuzzy semiprime ideals in semigroups. We have characterized completely regular semigroups by the properties of their   q   , -fuzzy quasi ideals. Moreover we have characterized semigroups that are semilattices of groups by   q   , -fuzzy quasi ideals.

In this paper the concept of Q-fuzzification of ideals of Γ-semigroups has been introduced and some important properties have been investigated. A characterization of regular Γ-semigroup in terms of Q-fuzzy ideals has been obtained. Operator semigroups of a Γ-semigroup has been made to work by obtaining various relationships between Q-fuzzy ideals of a Γ-semigroup and that of its operator semig...

Influenced by Higham, Mao and Stuart [9], several numerical methods have been developed to study the strong convergence of the numerical solutions to stochastic differential equations (SDEs) under the local Lipschitz condition. These numerical methods include the tamed Euler–Maruyama (EM) method, the tamed Milstein method, the stopped EM, the backward EM, the backward forward EM, etc. Recently,...

This paper presents a method based on fuzzy regression to analyze fatigue crack growth data, where the variations of the parameters are not only due to measurement errors but also system errors. A membership function is used to describe the system errors. Crack growth data under constant and random amplitude stress are analyzed and the results are compared with conventional least-squares regres...

and Applied Analysis 3 Table 1: Solitary wave Amp. 0.3 and the errors in L2 and L∞ norms for u, Q 1 , Q 2 , and Q 3 at t = 20, h = 0.125, Δt = 0.1, and −40 ≤ x ≤ 60. Method Time Q 1 Q 2 Q 3 L 2 for u L∞ for u Our method 0 3.9797 0.8104 2.5787 0 0 4 3.9797 0.8104 2.5786 3.6304e − 004 5.2892e − 005 8 3.9797 0.8104 2.5786 7.2873e − 004 5.8664e − 005 12 3.9797 0.8104 2.5787 1.0817e − 003 6.3283e − ...

maji et al. introduced the concept of fuzzy soft sets as a generalization of the standard soft sets and presented an application of fuzzy soft sets in a decision making problem. in this paper, we apply fuzzy soft sets to  -hyperrings. the concept of ( , q )       -fuzzy soft  -hyperideals of  -hyperrings is first introduced. some new characterizations are investigated. in particular, a...

In multicriteria decision making, the study of attribute contributions is crucial to attain correct decisions. Fuzzy measures allow a complete description of the joint behavior of attribute subsets. However, the determination of fuzzy measures is often hard. A common way to identify fuzzy measures is HLMS (Heuristic Least Mean Squares) algorithm. In this paper, the convergence of the HLMS algor...