جستجوی مقالات مرتبط با کلیدواژه « fuzzy » در نشریات گروه « ریاضی »

The main goal of this research is to develop a model for an integrated reporting framework with an Islamic approach. First, the researcher determined the framework's structure and components using theoretical foundations. Then, the structure and components of the framework were modified and finalized by the experts with the knowledge base. A structured form of the components was used twice to judge 32 experts in integrative reporting. The final model was determined by combining the Delphi technique with fuzzy logic and nonlinear data analysis. This template includes 7 Guidelines and 8 Content elements. In the Islamic section, several principles were determined as a guideline and several components were set to contribute to the content of the Islamic part of the report.

Unmanned Aerial Vehicles (UAVs) bring both potential and difficulties for emergency applications, including packet loss and changes in network topology. UAVs are also quickly taking up a sizable portion of the airspace, allowing Flying Ad-hoc NETworks (FANETs) to conduct effective ad hoc missions. Therefore, building routing protocols for FANETs is difficult due to flight restrictions and changing topology. To solve these problems, a bio-inspired route selection technique is proposed for FANET. A combined trustworthy and bioinspired-based transmission strategy is developed as a result of the growing need for dynamic and adaptable communications in FANETs. The fitness theory is used to assess direct trust and evaluate credibility and activity to estimate indirect trust. In particular, assessing UAV behavior is still a crucial problem in this field. It recommends fuzzy logic, one of the most widely utilized techniques for trusted route computing, for this purpose. Fuzzy logic can manage complicated settings by classifying nodes based on various criteria. This method combines geocaching and unicasting, anticipating the location of intermediate UAVs using 3-D estimates. This method guarantees resilience, dependability, and an extended path lifetime, improving FANET performance noticeably. Two primary features of FANETs that shorten the route lifetime must be accommodated in routing. First, the collaborative nature necessitates communication and coordination between the flying nodes, which uses a lot of energy. Second, the flying nodes' highly dynamic mobility pattern in 3D space may cause link disconnection because of their potential dispersion. Using ant colony optimization, it employs trusted leader drone selection within the cluster and safe routing among leaders. a fuzzy‐based UAV behavior analytics is presented for trust management in FANETs. Compared to existing protocols, the simulated results demonstrate improvements in delay routing overhead in FANET.

Due to the spread of the Internet and its pervasiveness, ``big data" is created daily. Processing this amount of data requires a system with high processing power. In fact, the production and collection of data from a wide range of different equipment and tools lead to the creation of large-scale databases. In dealing with large and unstructured databases and their management, there are always challenges. This study aims to present a model to increase the clustering accuracy of big data using a fuzzy clustering system based on data mining in a MatLab programming environment. For this purpose, first, the importance of each variable in the decision tree models in SPSSModeler software is determined, then with the help of these results, fuzzy rules are explained and a fuzzy inference system is formed in MATLAB software. This study uses data mining techniques such as C\&R Tree, Chaid and C5.0 to study the development of the FCM method to increase clustering accuracy in high volume data and related factors such as data preparation indicators, data type Data quality, data dimensions, data volume and number of clusters were evaluated as inputs and clustering accuracy index was evaluated as output. Then, with the help of these results, the rules of forming a fuzzy inference system were determined and by explaining the membership functions of the decision model, it showed what effect each input index has on the output index.

this goal, first the factors and indicators affecting the independent audit assurance services were defined based on the field analysis technique and then these factors and indicators were classified. According to this goal, this project is an innovative combination of consecutive and instrumental model type, design, and development model based on the design criteria of this model. Members of the Iranian Society of Certified Public Accountants have led to the definition of research model criteria by using the case study research method and semi-structured interviews of managers and partners of auditing firms and auditing firms. Based on interviews and analysis of previous studies, factors affecting the pricing model of independent audit assurance services were identified. 50 codes were examined which were classified into 9 groups based on axial coding. These factors include corporate governance, corporate ownership structure, internal factors of the company, external factors of the company, factors of the auditing firm, characteristics of managers and owners, financial and accounting factors, audit characteristics, and competitive market auditing. To determine the degree of importance of the identified criteria, the demitel phase group decision-making technique was used. Based on the obtained results, financial and accounting factors are at the highest level of impact (9.91). Financial and accounting factors refer to the criteria of free float, the percentage of ownership of major shareholders, audit risk, the ratio of total liabilities to total assets.

In this paper, the  notion of $ps-ro$ $\beta$-open (closed) fuzzy sets on a fuzzy topological space($fts$) has been introduced as a new tool to study $fts$, the properties of these sets are investigated and shown to be unrelated to the common concept of fuzzy $\beta$-open (closed) sets. Based on these fuzzy sets, $ps-ro$ fuzzy $\beta$-continuity and $ps-ro$ fuzzy $\beta$-open (closed) functions are proposed and they are also found to be different from the idea of fuzzy $\beta$-continuous and fuzzy $\beta$-open (closed) functions, respectively. Further, their  characterizations and relationships with existing allied concepts are investigated.

Vehicle scheduling problem is an important combinatorial optimization problem arising in the management of transportation companies. The problem consists of assigning a set of timetabled trips to a set of vehicles to minimize a given objective function. In this paper, vehicle scheduling problem under undeterministic conditions is considered. The paper states the necessity of considering the uncertain condition in the problem and reviews the different solution approaches to deal with the conditions of uncertainty. The main objective of this paper is to discuss the importance of considering uncertainty in vehicle scheduling problem, review the relevant literature and present some directions for future work.

In the development of networks, a new paradigm called flying adhoc networks (FANET) has evolved recently. FANET’s are comprised of unmanned aerial vehicles like drones, autonomous flights, etc. Drones are mainly used for surveillance, logistics, etc. On the verge of this FANET, many issues are arising regarding their proper usability, one among them being the routing of packets from the ground station to the destined drone. In this research work, we have developed a protocol for packet delivery called fuzzy adaptive routing protocol. This protocol uses factors like signal quality, signal to inference and noise ratio, hop count, and timestamp of the packet to decide on the broadcasting of route requests and reply messages. This protocol mainly contains two phases namely: controlled route request/reply broadcasting and optimal route selection for packet delivery. The performance of the proposed protocol is compared with recent techniques and the comparison depicts that the proposed protocol outperforms them and provides consistent service quality.

In this article, we have developed a deteriorated multi-item inventory model in a fuzzy environment. Here the demand rate is constant. Production cost and set-up cost are the most vital issue in the inventory system of the market world. Here production cost and set-up- cost are continuous functions of demand. Set-up-cost is also dependent on average inventory level. Deterioration cost is the most challenging issue in the business world. So here deterioration cost is dependent on inventory level and demand. Lead time crashing cost is considered the continuous function of leading time. In the real world all cost are not fixed. Due to uncertainty all cost parameters of the proposed model are taken as Generalized Triangular Fuzzy Number (GTFN). The formulated multi objective inventory problem has been solved by various techniques like as Geometric Programming (GP) technique, Fuzzy Programming Technique with Hyperbolic Membership Function (FPTHMF), Fuzzy Non-Linear Programming (FNLP) technique. Numerical example is taken to illustrate the model. Sensitivity analysis and graphical representation have been shown to test the parameters of the model.

Here, based on the Fibonacci polynomials, a new collocation method is presented in order to solve the system of linear fuzzy Volterra integral equations of the second kind. By using this method, these systems are reduced to a linear system of algebraic equations that are easily solvable. Also, the existence of the solution and error analysis of the proposed method are discussed. Finally, in order to show the importance and application of the proposed method, we have used several illustrative examples. The method is computationally very attractive and gives very accurate results. Easy implementation and simple operations are the essential features of the Fibonacci polynomials.

One of the important challenges in Graphical models is the problem of dealing with the uncertainties in the problem. Among graphical networks, fuzzy cognitive map is only capable of modeling fuzzy uncertainty and the Bayesian network is only capable of modeling probabilistic uncertainty. In many real issues, we are faced with both fuzzy and probabilistic uncertainties. In these cases, the proposed method of this paper can take into account both types of uncertainty with a new and different approach. In this method, we avoid fuzzy transformations to probabilities and vice versa, and fuzzy uncertainties and probabilities are considered jointly. For this purpose, in the original graphical model, first, the type of uncertainty of each node is identified, and accordingly two separate fuzzy and probabilistic networks are constructed. In these networks, relations between nodes are expressed in terms of a set of rules. In each network, fuzzy and probabilistic inference is individually constructed and ultimately the values obtained from each network are combined. This method has been tested on a real problem of localization in wireless sensor networks. In this case, a sensor with uncertain location should be able to predict its location from the received power of its adjacent sensors. In the given scenario, 60 sensors with uncertain locations and 121 sensors with a specific location are considered. Meanwhile, the average location error of sensors has been used to evaluate the methods. The simulation results show the efficiency of the proposed method well.

In this study, the aim is to propose a new method for fuzzification of nested dummy variables. The fuzzification idea of dummy variables has been acquired from non-linear part of regime switching models in econometrics. In these models, the concept of transfer functions is like the notion of fuzzy membership functions, but no principle or linguistic sentence have been used for inputs. Consequently, for the non-linear part including transfer function, there is no reason why the different types of functions such as logistic are used. Therefore, in order to solve the aforementioned problem like the regime switching models, the transfer functions are considered for dummy variables. However, the presented transfer functions are proposed by fuzzy clustering membership function. Finally, using fuzzy logic, the membership functions of clusters are combined with each other and constitute the fuzzy nested regimes. The suggested model has been used in financial data of Iran's stock in order to examine the equity premium Puzzle. The results of using above model helped in modeling appropriate second-order moments in consumption capital asset pricing model.

The TOPSIS process is one of the most comprehensive systems designed for decision making with multiple criteria, since this technique enables formulation of the problem as decision matrix, as well as the possibility of considering different quantitative and qualitative criteria in the problem. Fuzzy TOPSIS methods have been introduced to make fundamental decisions that make decisions decisions in these ways, including the lack of precision in fuzzy calculations, which can be attributed to the lack of use of the principle of "Zadeh” in the definition of operators. For example, the multiplication of two triangular fuzzy numbers will not necessarily be triangular fuzzy. However, in most of the fuzzy TOPSIS methods, this is assumed. In this paper, we attempt to first define the ideal vector of false algebra, then two functions are designed for this purpose, then the optimal of these functions can be found using GA. All steps are based on the principle of "Zadeh”".

In recent years, Fuzzy differential equations are very useful indifferent sciences such as physics, chemistry, biology and economy. It should be noted, that if the equations that appear to be uncertain, then take help of fuzzy logic at these equations. Considering that most of the time analytic solution of such equations and finding an exact solution has either high complexity or cannot be solved, we applied numerical methods for the solution. The topics of fuzzy differential equations have been rapidly growing in recent years. So far, many methods have been presented to solve the first-order differential equations. Not many studies have been conducted for numerical solution of high-order fuzzy differential equations. In this research, first, the equation by reducing time, we become the first-order equation. Then we have applied Adam-Moulton multi-step methods for the initial approximation of one order differential equations. Finally, we examine the accuracy of method by presenting examples.

The Bosphorus (Istanbul Strait) that connects the continents of Europe and Asia is too complex due to geographical conditions and have a continuously increasing maritime traffic. The length of the Bosphorus which connect the Black Sea and the  Marmara Sea is 29.9 km. The number of ships (Tanker, general Cargo and bulk carrier) passing through the Istanbul strait was recorded as 42553 in 2016 and these transit ships cause more than half of the amount total ship emissions in the Bosphorus. The aim of this study is to estimate the PM amounts emitted from transit vessels passing through the Bosphorus by using fuzzy inference system in MATLAB. Total emissions from ships are expressed by surfaces allowing the analysis of the data according to the gross tonnage and the types of ships.

In this paper, a method is introduced which reduces the harmonic distortion of the inverter output current, connecting the fuel cell to the AC load. Using FUZZY LOGIC-PI (FLPI), the controlling method is engaged in optimum tuning of the PI controller coefficients of this converter. This converter has only one DC-AC boost inverter and simultaneously has the task of increasing voltage level and generating the AC power. In order to control the inverter output voltage, an internal current control loop and an external voltage control loop are used. Designing the control of each loop is done separately based on FLPI controller. Simulation results for proposed fuel cell energy system in the presence of this controller imply good performance of FLPI and suitable mitigation of inverter output current ripple