جستجوی مقالات مرتبط با کلیدواژه « ‎Fuzzy Logic‎ » در نشریات گروه « ریاضی »

Today, the military bodies of different countries have identified the existence of a condition assessment system in order to optimize the process of maintenance and repair of military buildings and equipment as a need and are looking for a suitable answer. The issue of less and/or outright lack of knowledge and uncertainty in modeling and decision-making plays a crucial part in many engineering and especially military difficulties, resulting in designers and engineers being unable to obtain definitive solutions for the problems under discussion. This study develops a fuzzy logic application for representing the uncertainty inherent in the problem of warehouse maintenance scheduling. The relative risk score (RRS) approach, one of the most prevalent methodologies for maintenance assessment, is combined with fuzzy logic to achieve the goal. Based on expert knowledge, the suggested model is run on the MATLAB® fuzzy logic toolbox using the Mamdani algorithm. A representative case study is used, and a comparison is made between the traditional risk assessment technique and the suggested model. The findings show that the suggested model produces more accurate, exact, and certain results, allowing it to be used as an intelligent risk assessment tool in many engineering settings.

In today's age, the Internet has become the most important source of information and its supply for many goods and services due to its ease of use, wide range and high speed. The unique characteristics of the Internet and its complete superiority over other markets have led many organizations to expand their services and products in the Internet markets. Therefore, the Internet environment has become a very competitive environment for organizations. In the process of buying and checking user and consumer behavior, goods or services will be exchanged, and these goods or services can be information, physical or virtual products, and even emotions. Many kinds of research have been conducted in the analysis of users' behavior, but these researches were not highly accurate, therefore, in this research, an attempt was made to provide a solution for recommending items based on users' past behaviors to improve the accuracy of past methods. This proposed method can eliminate the noise in the data by using fuzzy logic and hierarchical analysis which is used for the phase of selecting the effective features and also works on more effective data to increase the accuracy. The tree proposed in this research has the lowest possible height and this causes the computational overhead to be greatly reduced. The proposed method was compared with several competing methods and the results indicate the superiority of the proposed method over the compared methods.

When hearing the term fuzzy logic, the first thing that the mind immediately goes to is the field of non-deterministic arguments, and if we want to use a term close to it to make its meaning a little clearer, we can say that fuzzy logic is the same as Armagon's logic. It is an approximation that, ironically, the literal meaning of the word "argument" also refers to the clarification of something by the same thing (function input or armagon). Now the basic question is that while the educated mind of man has considered logic as equivalent to the mechanism of definite perceptions and accurate results for more than a thousand years, how can we talk about the invention of a logical method that is based on uncertainty? This question is doubly important from the aspect that our approximate arguments in the context of our lived life are more than our exact, definite and unambiguous arguments, that is, the same arguments that classical logic, whether in its old or new language, He leaves it aside and does not pay attention to them, and of course he is forced to do this because he does not have the tools to analyze these arguments. This is where we unknowingly remind ourselves of Bacon's angry regret, who considers Aristotelian logic to be a fantasy that causes waste and diversion of great talents over many centuries, a continuous and constant effort without helping a person to improve the quality of his life; A bunch of delusional nonsense, the result of which, perhaps in the most optimistic state, is Scott's adversary and the victory of futility in verbal disputes, of course, if we do not say only pointless boasting and scrutiny based on rhetoric, the fruit of which is the explanation of rational axioms. If Mr. Bacon knew about fuzzy logic, he would have corrected his view of logic, and to be more precise, he would have separated Aristotle from logic, not logic from science. One of the most important advantages of fuzzy logic is that it can introduce a way to enter the knowledge and experiences of experts into intelligent systems that are based on sensitive and practical decisions and choices and develop the previous absolute correct and incorrect logic. The basis of the analysis in the present research is to examine the components of two types of logic, i.e. classical logic and fuzzy logic, and clarify the understanding of the logical concepts and philosophical foundations of classical logic, as well as comparing it with the concepts of fuzzy logic. The research results indicate: these two types of logic have fundamental differences from an epistemological point of view; Aristotle's epistemological foundation is based on the certainty-oriented duality of "either this/or that", while the fuzzy epistemological foundation is based on a multi-valued system and that both logics are a contradictory understanding of what the mind and the external world are and the connection between They both give hands.

Micanorm-based logics with a weak form of associativity are introduced and their completeness results are addressed. More concretely, first the basic wa$_{t}$-uninorm logic \textbf{WA$_{\textbf{t}}$BUL} and its axiomatic extensions are introduced as $[0, t]$-continuous wa$_{t}$-uninorm analogues of the logics based the $[0, 1)$-continuous uninorms. Next algebraic structures characterizing the logics are introduced along with algebraic completeness results. Third, wa$_{t}$-uninorms are introduced as uninorms with weak $t$-associativity instead of associativity and associated properties are discussed. Finally, by virtue of Yang--style construction, it is verified that the logics based on wa$_{t}$-uninorms are complete on unit real interval $[0, 1]$, i.e., so called \emph{standard} complete.

This paper presents the design of fuzzy electronic markets based on several agents. The character of the buyer and seller agents influences their behavior in the market. Various factors play an important role in the precise, real, flexible and attractive design of a market. This research aims to model the market and the character of intelligent agents based on fuzzy logic. In the market, sellers are known with different titles or credits. Results confirmed that sellers with high levels of personality would gain more credit than other sellers, consequently, they earn more sales and profit. In this model, when a seller does not tell the truth about his product, the customer might also suspect the honesty of the seller about the quality of other products. In fact, the seller is recognized as a non-reputable person who possibly conceals the truth regarding the quality of his products. Therefore, the costumer will focus on sellers with high reputation for the future purchases. If the costumer finds no reputable seller, he will buy from those sellers whose credibility has not yet been evaluated (disreputable seller). He only purchases again from the non-reputable sellers if he could find neither the reputable seller nor the disreputable seller. Salesmen can offer promotions for attracting customers which is the most important goal of sellers. One of the promotions is giving discounts to buyers who have made more purchases from specific sellers or have brought more profit for the seller. In the proposed model, the discount was an important factor in attracting customers, which was achieved by sellers with high personalities. This feature was not included in previous models, and we have implemented this model with Aglet, and MATLAB. Results indicated that fuzzy agents modelling buy/sell based on their personality are more satisfied than sell/buy agents using only fixed bids.

Currently, cloud computing provides the necessary infrastructure and software services to provide the requested services needed by users on the Internet. Due to the spectacular growth of cloud computing, the number of users and the number of demands are increasing rapidly, which creates a high workload on servers and computing resources. In this situation, for the optimal use of resources, the need for an efficient and effective management approach is fully felt. For this purpose, game theory has been used. The game structure is designed in such a way that the leader (leader) owns a large number of resources and plans the allocation of resources based on the request received from mobile users. The goal from the leader's point of view is to minimize the cost of using the resources located in the fog nodes, on the other hand, the goal considered from the mobile users' point of view is to minimize the cost of responding and transmitting the message to the desired fog node. For this purpose, the entire region is divided into regions and a fog node is considered for each region. The main goal of this research is to reduce the average delay in the provision of services related to Internet of Things applications in cloud computing platforms. For this purpose, an attempt is made to provide a new method for allocating multiple tasks in mobile collective monitoring based on fog computing in the Internet of Things using the inverse Stackelberg game theory with the help of fuzzy logic and deep reinforcement learning algorithm.

Internet of Things describes a situation in which a large number of devices (things) are connected through a number of sensors via Internet, and lack of cooperation of some nodes in providing service to other nodes might interrupt the connection of some things, degrading network efficiency. A multi-phase mechanism based on Game theory and direct/indirect fame has been designed to motivate the selfish and malicious nodes to cooperate in IoT, which begins by deploying nodes in the IoT network. In the first phase, the nodes are grouped into clusters with cluster-heads for data collection. In the second phase, a multiplayer and dynamic game is executed while forwarding their data packet or others’ data packet. Nodes can pick their strategy when data packet forwarding in the third phase (Fuzzy logic reputation). Nodes will determine the neighboring node reputation by using fuzzy system. The amount of reputation of each of the nodes has been realized and finally, with the help of second phase and fuzzy logic, each node is decided to be cooperate or selfish nodes and in case of head clusters and fuzzy logic in some cases, the opportunity node will be reestablished to cooperate in network activities otherwise the node will be isolated. The effectiveness of the proposed solution has been assessed and the parameters of non-cooperative node detection accuracy, positive and negative warning rates, network PDR, and average endto-end latency perform better compared to other previous methods.

This paper is motivated by high dose rate brachytherapy treatment planning problems which involve the specification of the movement schedule of a radiation source so that the target volumes are adequately covered with sufficient doses and organs at risk are not radiated beyond the clinical acceptance threshold. It utilizes four powerful multi-objective evolutionary algorithms (MOEA), which create a set of equally-weighted Pareto optimal solutions instead of only one and produce better results compared to other optimization methods. These algorithms include non-dominated sorting genetic algorithms, Pareto envelope-based selection algorithm, non-dominated ranking genetic algorithm, and strength Pareto evolutionary algorithm. The results indicate that the last algorithm uses the dependency between decision variables to solve them efficiently and is the best type of MOEA both in terms of convergence criteria and solution diversity maintenance for the brachytherapy problems.

Human resource productivity is one of the main concerns in organizations. In total,100 factors According to on three main sources: 1- Opinions of experts and academic professors, 2- Using project technical documents, 3- Previous similar research studies and related scientific sources, were identified and categorized into four groups: plan human resource management, acquire project team, develop project team, and manage project team. Questionnaires were distributed among 103 members of the target population who were active construction contractors in Iran. The questionnaires were analyzed using two methods Relative Importance Index (RII) and the Fuzzy Logic (FL). Ten factors that had the highest impact, based on the two methods, on HRP efficiency in the projects were identified and compared. The results of fuzzy logic and RII method showed that both methods were highly similar in terms of outcomes. In addition, the results indicated that “lack of proper communication between the technical office and workshop” was the most important factor based on the two techniques mentioned.

The application of fuzzy linear programming and optimization techniques has a rich history in various domains. In recent years, the rise in employee terminations within large companies has underscored the significance of employee performance and its impact on organizational progress. To address this issue, it becomes crucial to determine the appropriate number of employees required to effectively execute company projects, considering employee performance and organizational needs. Additionally, it is essential to identify an optimal employee count as a benchmark prior to hiring. This optimal value can be achieved through the utilization of optimization methodologies, such as fuzzy linear programming. This research paper presents a solution to the employee hiring problem in a factory by utilizing the fuzzy linear programming method. The findings reveal that increasing the number of hires does not necessarily correlate with enhanced performance. The findings of this paper enable organizations to make informed decisions regarding employee recruitment and enhance overall operational efficiency.

The reliability of software product is seen as critical quality factor that cannot be overemphasized. Since real world application is loaded with high amount of uncertainty, such as applicable to software reliability, there should be a technique of dealing with such uncertainty. This paper presents a reliability model to effectively handle uncertainty in software data to enhance reliability prediction of software at the early (requirements and design) stages of Software Development Life Cycle (SDLC). In this paper, a hybrid methodology of Takagi Sugeno Kang (TSK)-based Interval Type-2 Fuzzy Logic System (IT2FLS) with Artificial Neural Network (ANN) learning is employed for the prediction of software reliability. The parameters of the model are optimized using Gradient Descent (GD) back-propagation method. Relevant reliability software requirement and design metrics and software size metrics are utilized as inputs. The proposed approach uses twenty-eight real software project data. The performance of the model is evaluated using five performance metrics and found to provide output values that are very close to the actual output showing better predictive accuracy.

This paper proposes an application of a Takagi-Sugeno fuzzy model to the prediction of complex mass transfer behavior in smart toilet bidet systems‎. ‎The model is constructed through the integration of fuzzy logic theory‎, ‎nonlinear autoregressive moving average exogenous input models‎, ‎neural networks‎, ‎and data clustering algorithms‎. ‎To develop the model for estimating the air quality of the smart toilet-bidet system‎, ‎many datasets are collected from a smart toilet bidet model equipped with an automatic odor/bacteria suction system using Sulfur hexafluoride ($SF_6$) gas‎. ‎Many case studies were carried out as a function of the suction flow rate‎, ‎suction angle‎, ‎the number of suction holes‎, ‎and suction hole size‎. ‎The inputs for training the fuzzy model are the size‎, ‎number‎, ‎and angles of suction holes‎, ‎whereas its output is the undesirable gas concentration‎. ‎The trained fuzzy model is tested using different datasets‎. ‎Modeling and testing results show the effectiveness of the fuzzy model in predicting the gas concentration of the toilet bowl‎. ‎The proposed fuzzy model is expected to be useful in the implementation of smart toilet bowl systems in the near future‎.

Economic and financial phenomena are highly complex and‎ ‎non-linear‎. ‎However‎, ‎surprisingly‎, ‎in many cases‎, ‎these phenomena‎ ‎are accurately described by linear models‎ -- ‎or‎, ‎sometimes‎, ‎by‎ ‎piecewise linear ones‎. ‎In this paper‎, ‎we show that fuzzy‎ ‎techniques can explain the unexpected efficiency of linear and‎ ‎piecewise linear models‎: ‎namely‎, ‎we show that a natural‎ ‎fuzzy-based precisiation of imprecise (``fuzzy'') expert knowledge‎ ‎often leads to linear and piecewise linear models‎.‎We show this by applying invariance ideas to analyze which membership functions‎, ‎which fuzzy ``and''-operations (t-norms)‎, ‎and which‎ ‎fuzzy implication operations are most appropriate for applications to economics and finance‎. ‎We also discuss which expert-motivated nonlinear models should be‎ ‎used to get a more accurate description of economic and financial‎ ‎phenomena‎: ‎specifically‎, ‎we show that a natural next step is to add cubic‎ ‎terms to the linear (and piece-wise linear) expressions‎, ‎and‎, ‎in general‎, ‎to consider polynomial (and piece-wise polynomial) dependencies‎.

With this paper the authors try to newly reflect on Zadeh's concept of a fuzzy set‎. ‎The departing point is the fact that not only fuzzy sets originate in Language‎, ‎but that they are just 'linguistic entities' genetically different from the concept of 'crisp sets' whose origin is either in a physical collection of objects‎, ‎or in a list of them‎.Thus‎, ‎a new definition of a fuzzy set is presented by means of two magnitudes‎: ‎A qualitative one‎, ‎a graph‎, ‎the basic magnitude‎, ‎and a quantitative one‎, ‎a scalar magnitude‎. ‎If the first reflects the language's relational ground of the fuzzy set‎, ‎the second‎ - ‎and thanks to 'measuring the meaning of words'‎ -, ‎reflects the (numerical) extensional state in which it currently appears‎.‎Since the second‎, ‎the scalar magnitude‎, ‎is essential for the applications‎, ‎it is also introduced the concept of a 'working fuzzy set' by taking into account the numerical function‎, ‎the meaning's measure or the membership function‎. ‎The working fuzzy set‎, ‎that enlarges the corresponding fuzzy set‎, ‎allows us to see the 'same fuzzy set' with different membership functions‎, ‎that is‎, ‎the same graph appearing in different extensional states‎. ‎Notice that a 'working crisp set' is but the same crisp set‎.

Selecting the optimal stock portfolio is one of the most important issues in the field of financial research, which tries to choose the optimal combination of assets in order to create maximum utility for the investor, Given that the return on securities in the real world is often vague and inaccurate, one of the most important investment challenges is uncertainty about the future. In this paper the problem of selecting and optimizing securities portfolios with different modeling goals has been solved and compared. The designed models have considered both the nature of the portfolio selection issue and the considerations considered by the shareholder in the portfolio selection. The uncertainty quality of the future return of a given portfolio is estimated using fuzzy LR numbers, while its return torques are measured using possibility theory. The most important purpose of this paper is to solve the problem and compare portfolio selection models with simultaneous optimization of two, three, and four objectives. For this purpose, the NSGA-II genetic algorithm is used and the mutation and intersection operators are designed specifically to generate possible solutions to the cardinality constraint of the problem. Finally, the efficiency and performance of the models in case of using fuzzy logic and not using it have been compared and it has been determined that the use of fuzzy logic and possibility theory leads to the formation of portfolios with higher performance and higher efficiency.