In this paper, we deal with games of fuzzy payoffs problem while there is uncertainty in data. We use the trapezoidal membership function to transform the data into fuzzy numbers and utilize the three different ranking function algorithms. Then we compare between these three ranking algorithms by using trapezoidal fuzzy numbers for the decision maker to get the best gains
The purpose of this study is to investigate learners' listening comprehension problems with Englishlectures. The study was guided by research question (What are the listening comprehension problems learners have with lectures in English).Furthermore; the main significant goal were declared through conducting this study, as well as providing some procedures of distribution the questionnaire of the study.Moreover, it presents several definitions of listening. This study definitely depends on questionnaire instrument to gathering the required data. The participants of the study were 30 learners completed their secondary school and joined at the college. Based on the findings among the five factors (text, speaker, task, environment, and list
... Show MoreIn this paper, we introduce the concept of almost Quasi-Frobcnius fuzzy ring as a " " of Quasi-Frobenius ring. We give some properties about this concept with qoutient fuzzy ring. Also, we study the fuzzy external direct sum of fuzzy rings.
In the present study, Čech fuzzy soft bi-closure spaces (Čfs bi-csp’s) are defined. The basic properties of Čfs bi-csp’s are studied such as we show from each Čfs bi-csp’s (
The aim of this work is to a connection between two concepts which are an interval value fuzzy set and a hyper AT-algebra. Also, some properties of these concepts are found. The notions of IVF hyper AT-subalgebras, IVF hyper ideals and IVF hyper AT-ideals are defined. Then IVF (weak, strong) hyper ideals and IVF (weak, strong) hyper AT-ideals are discussed. After that, some relations among these ideals are presented and some interesting theorems are proved.
In this paper, we introduce three robust fuzzy estimators of a location parameter based on Buckley’s approach, in the presence of outliers. These estimates were compared using the variance of fuzzy numbers criterion, all these estimates were best of Buckley’s estimate. of these, the fuzzy median was the best in the case of small and medium sample size, and in large sample size, the fuzzy trimmed mean was the best.
The concept of bipolar fuzzy ideals in a TM-algebra was introduced and some properties of these ideals are investigated. Also, a few relations between a bipolar fuzzy ideal and T-ideal are discussed. A new bipolar fuzzy set with a homomorphism of TM-algebra is defined. The Cartesian product of bipolar fuzzy T-ideals in Cartesian product TM-algebras is given.
In this paper, a new class of nonconvex sets and functions called strongly -convex sets and strongly -convex functions are introduced. This class is considered as a natural extension of strongly -convex sets and functions introduced in the literature. Some basic and differentiability properties related to strongly -convex functions are discussed. As an application to optimization problems, some optimality properties of constrained optimization problems are proved. In these optimization problems, either the objective function or the inequality constraints functions are strongly -convex.
Simulation of the Linguistic Fuzzy Trust Model (LFTM) over oscillating Wireless Sensor Networks (WSNs) where the goodness of the servers belonging to them could change along the time is presented in this paper, and the comparison between the outcomes achieved with LFTM model over oscillating WSNs with the outcomes obtained by applying the model over static WSNs where the servers maintaining always the same goodness, in terms of the selection percentage of trustworthy servers (the accuracy of the model) and the average path length are also presented here. Also in this paper the comparison between the LFTM and the Bio-inspired Trust and Reputation Model for Wireless Sensor Network
... Show MoreThe aim of this paper is to approximate multidimensional functions f∈C(R^s) by developing a new type of Feedforward neural networks (FFNS) which we called it Greedy ridge function neural networks (GRGFNNS). Also, we introduce a modification to the greedy algorithm which is used to train the greedy ridge function neural networks. An error bound are introduced in Sobolov space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result in [1]).