Use Of Fuzzy Logic Research Articles

Determination of Giving ‘Tips’ Using Fuzzy Logic To Determine Restaurant Customer Service Satisfaction

Fuzzy logic can be used to represent obscurity based on terms such as high, old, hot, cold, and so on. Fuzzy logic can extend the range of truth values to all real numbers in intervals between 0 and 1. Numbers in this interval represent the possibility that the statement given is “true” or “wrong”. The use of fuzzy logic can be used in everyday life, such as in the fields of medicine, engineering, agriculture, transportation, environmental science and also in economic and business applications. As in this study, it was used to calculate the ideal amount of ‘tips’ for restaurant waiters based on the service provided by restaurant waiters. The method used in this study is to use Mamdani fuzzy logic with the process of defuzzyfication using the Centroid of area (COA). The results of this study are that it can be determined the value crisp of giving tips to restaurant waiters is 45 thousand rupiah provided that the service provided for service = 7 (Excellent) and food service = 8 (Delicious)..

WITHDRAWN: Analysis of fuzzy logic and fractals in DNA sequences based on human signature

Abstract The structure of any DNA sequence is most certainly non-deterministic. Fuzzy logic is used inaccuracies and uncertainties of any situation. Fractals are infinitely complex pattern that are self similar across the different scales. It is irregular shape what we see in nature. The main aim of this paper is use of Fuzzy logic in Fractals based on DNA sequences using human signature. DNA fingerprinting is the analysis of DNA patterns to identify specific individuals. It is used to diagnosis inherited disorders and developing cure for that, biological evidence. It is also helpful in solving crimes by comparing the pattern of DNA profile with those victim and suspect, The comparison is then used to draw conclusion whether the suspect was present at the crime scene.

Fuzzy logic and fuzzy information technology

With the introduction of the concept of fuzzy logic by Lotfi Zadeh, the world of information technology has changed dramatically, because now there are not two states of the system, as in Boolean logic, but also intermediate states. In the twentieth century, the world was faced with the fact that existing systems made mistakes due to the accuracy of calculations, which is not applicable to solving real-world problems, but is applicable to solving mathematical problems. It was necessary to create systems that would mimic human thinking to solve complex problems and make decisions in an environment of uncertainty and constantly changing conditions. The article gives the concept of fuzzy logic, shows the difference between fuzzy logic and Boolean logic. The authors present the history of the concepts of fuzzy logic, fuzzy set, and fuzzy system. Indicate the stages of development of fuzzy logic and the main results of the development of fuzzy logic at these stages. The article presents the architecture of a fuzzy system, explains the main stages of passing data in this system to solve a specific problem. The authors presented a solution to a small decision-making problem, namely, the selection of boxers in the national team, based on fuzzy logic, using the functionality of the MATLAB software product and the Fuzzy Logic Modeler extension. The article gives the concept of hard and soft computing, their differences from each other. The authors present the main soft computing technologies based on fuzzy logic, and describe their algorithms. The following are examples of the use of fuzzy logic in the modern world, such as control systems, fuzzy expert systems, automatic systems. Thus, the authors focus on the role of fuzzy logic and fuzzy technologies in everyday life.

Analysis of fuzzy logic methods for forecasting customer churn

The object of research is the process of predicting the churn of customers of telecommunications companies based on fuzzy logic and neural networks. The research carried out is based on the application of an approach that is implemented through the combined use of fuzzy logic and neural networks. The main assumption of the study is the hypothesis that the use of a fuzzy neural network formed on the basis of fuzzy logic algorithms can improve the accuracy of predicting customer churn relative to available solutions. This result can’t be achieved neglecting the existing resource constraints and requirements, which must be determined separately for each case of research. The relevance of the problem of forecasting customer churn for companies with a large number of users is considered. A model for predicting customer churn is proposed based on the combined use of fuzzy logic and neural networks. The main feature of this approach is that a test sample of normalized data is used at the basis of fuzzy neural networks, which are processed to form the parameters of membership functions that correspond to the inference system, that is, conclusions are made on the basis of a fuzzy logic apparatus. Also, to find the parameters of the membership function, neural network algorithms are used. Such systems can use previously known information, learn, gain new knowledge, predict time series, perform image classification, and besides, they are quite visual to the user. The application of methods of fuzzy logic is considered, they make it possible to obtain a result in the form of a fuzzy inference. The expediency of choosing these methods is explained by the fact that they were previously used in fuzzy automatic control systems and showed sufficiently high quality results. The expediency and prospects of using the proposed approach in the problem of predicting the outflow of customers of telecommunications companies are shown, and the results of software implementation are presented.

Optimal Fuzzy Energy Trading System in a Fog-Enabled Smart Grid

With the recent technological advancements, it has become possible to conceive numerous valuable applications for efficient utilization of energy resources in a smart grid. As distributed energy generation and distributed storage systems become cost-effective, trading energy becomes a lucrative alternative for both prosumers and manufacturers. In this paper, we make use of fuzzy logic to propose a system for optimal energy trading in a fog-enabled smart grid set-up. The existing systems in this realm have inherited issues of network latency, computational expensiveness, information availability, scalability, and performance. Some systems require a specialized transmission line for energy trading and plenty of them based on the dedicated producer-consumer model, putting limits to their practical effectiveness. Our framework makes use of fog-computing infrastructure to address scalability, information availability, and network latency issues. We exploit the fuzzy logic paradigm to handle the issues with crisp values and to improve the computational efficiency of the system. Our model of energy-trading system incorporates various input parameters to decide on the excess energy, including real-time price, time of day, outdoor temperature, buyers’ interest, and storage capacity. Simulation results show that our proposed system possesses promising potential to maximize the profit of energy trading and to minimize electricity usage from the main grid.

Technology Stack Selection Model for Software Design of Digital Platforms

The article is dedicated to the development of a mathematical model and methodology for evaluating the effectiveness of integrating information technology solutions into digital platforms using virtual simulation infrastructures. The task of selecting a stack of technologies is formulated as the task of selecting elements from sets of possible solutions. This allows us to develop a mathematically unified approach to evaluating the effectiveness of different solutions, such as choosing programming languages, choosing Database Management System (DBMS), choosing operating systems and data technologies, and choosing the frameworks used. Introduced technology compatibility operation and decomposition of the evaluation of the efficiency of the technology stack at the stages of the life cycle of the digital platform development allowed us to reduce the computational complexity of the formation of the technology stack. A methodology based on performance assessments for experimental research in a virtual software-configurable simulation environment has been proposed. The developed solution allows the evaluation of the performance of the digital platform before its final implementation, while reducing the cost of conducting an experiment to assess the characteristics of the digital platform. It is proposed to compare the characteristics of digital platform efficiency based on the use of fuzzy logic, providing the software developer with an intuitive tool to support decision-making on the inclusion of the solution in the technology stack.

Movement of a Solar Electric Vehicle Controlled by ANN-based DTC in Hot Climate Regions

Vehicle autonomy presents the most complex problem for modern commercialized solar electric vehicle (SEV) propulsion systems. The power supplied by electric vehicles’ batteries is limited by the state of charge, the type of battery, and its level of technological development. This study’s aim was to resolve the problem of energy variation at several velocities and under different road topology conditions. Several works related to the use of fuzzy logic confirm that classical regulators have such advantages over fuzzy regulators as short processing times and mathematical precision. Therefore, the hybrid power source is presented as the best solution for energy management, and it is composed of a solar panel (PV) and a nickel metal hydride battery. The PV system is connected to the SEV via a boost converter that is controlled using the maximum power point tracking technique. In this paper, we used an intelligent PI regulator for direct torque control, which introduced a certain degree of intelligence into the regulation strategy. Indeed, this approach of associating the PI regulator with the fuzzy rules-composed supervisor allowed us to take advantage of both the PI’s mathematical precision and the adaptability, flexibility, and simplicity of fuzzy linguistic formalism. Because of its dynamic capabilities, an adaptive PI regulator was substituted to achieve high speeds and a satisfactorily vigorous performance while quickly compensating for the disturbances that were expected to possibly take place on the regulation chain. The present study’s results confirm that the proposed control approach increased the utility of SEV autonomy under several speed variations. Moreover, the industry’s future offerings must take the option of hybrid power management into consideration during this type of vehicle’s manufacturing phase

A fuzzy approach for sarcasm detection in social networks

The great success of social networks is due to their ability to offer Internet users a space of free expression where they can produce a large amount of information which provides every day with a new challenge for data analysts. The ease of use of social media encourages users to increasingly express their opinions either by using simple words expressing feelings or by using irony and sarcasm. The new challenges are to extract and analyze this mass of information which can then be used in different applications such as sentiment analysis and sarcasm detection. Sarcasm detection is a subarea of sentiment analysis, opinion mining, and emotion mining which are all representing the process of automatic identification of people’s orientation or sentiment toward individuals, products, services, issues, and events. Sarcasm detection, which is the fact of deciding if a text is ironic or not, could be used, for example, to improve the precision of the sentiment analysis. In most of the existing approaches, sarcasm detection is a binary classification; each text is classified as sarcastic or non-sarcastic; however, since tweets are generally written by humans and humans are by default fuzzy in their emotions and expressions, we can’t 100% confirm that a text is sarcastic or not. In addition, tweets are expressed in natural language which is full of ambiguity and non-precision, which motivates us more to adopt fuzzy logic, not just to detect sarcasm but to give it a score. In this manuscript, we propose a fuzzy sarcasm detection approach using social information such as replies, historical tweets and likes, etc multiplying each by a degree of importance. The evaluation shows that the use of fuzzy logic has led us to improve the precision metric of the classification and to improve the accuracy of our approach. Using degrees of importance gave us the best values for recall, precision, and accuracy measures compared to existing approaches.

DETERMINATION OF THE DEGREE OF RISK OF EMERGENCIES WITH THE USE OF FUZZY LOGIC

DETERMINATION OF THE DEGREE OF RISK OF EMERGENCIES WITH THE USE OF FUZZY LOGIC

R-method: A simple ranking method for multi-attribute decision-making in the industrial environment

A simple multi-attribute decision-making method based on ranking of alternatives and attributes is proposed in this paper. The method ranks the alternatives with respect to each of the attributes based on the corresponding performance measures. Similarly, the ranks are assigned to the attributes based on their importance as perceived by the decision maker. The ranks assigned to the alternatives with respect to each of the attributes and the ranks assigned to the attributes are converted to appropriate weights and the final composite scores of the alternatives are computed using these weights. An interesting feature of the proposed method is that the qualitative attributes (i.e. the attributes expressed in linguistic terms) need not require the use of fuzzy logic. The proposed method is very simple and useful in situations of limited time availability, presence of qualitative attributes, imprecise/incomplete/partial data, and decision maker’s limited attention and capability to process the information. The proposed method is proved easier and better compared to the other widely used decision-making methods. The proposed method will be tested further on more realistic problems of the industrial environment and the results will be reported soon.

ВИКОРИСТАННЯ НЕЧІТКИХ НЕЙРОННИХ МЕРЕЖ В ЗАДАЧАХ КОНТРОЛЮ І ДІАГНОСТИКИ ТЕХНІЧНОГО СТАНУ АВІАЦІЙНОГО ДВИГУНА ТВ3-117 У ПОЛЬОТНИХ РЕЖИМАХ

The subject matter of the article is TV3-117 aircraft engine and methods for monitoring and diagnosing its technical state. The goal of the work is to substantiate the use of the Wang-Mendel fuzzy neural network for the practical implementation of fuzzy expert systems for monitoring the technical state of aircraft gas turbine engines, in particular, TV3-117, based on the use of fuzzy logic. The following methods are used: methods of probability theory and mathematical statistics, methods of neuroinformatics, methods of information systems theory and data processing. Conclusions: The results of the research have shown that artificial neural networks and systems with fuzzy logic are similar to each other, however, each of them has its own advantages and disadvantages. This conclusion was taken as a basis for creating fuzzy neural networks. Such networks build a solution based on the apparatus of fuzzy logic, however, membership functions are tuned using learning algorithms for artificial neural networks. In addition, such networks can not only learn, but are also able to take into account a priori information. By their structure, fuzzy neural networks are similar to multilayer networks, for example, with a network that learns according to the backpropagation algorithm, but the hidden layers in fuzzy networks correspond to the stages of the fuzzy system: the first layer introduces fuzziness based on the given characteristics of the inputs; the second layer defines a lot of fuzzy rules; the third layer has the function of sharpening. Each of these layers has a set of parameters, the setting of which is performed in the same way as setting up a conventional neural network . The fuzzy expert system, implemented using the Wang-Mendel fuzzy neural network, can be used in the on-board system for control and diagnostics the technical state of aircraft gas turbine engines, in particular helicopter ones – TV3-117. Keywords: aircraft engine, neural network, expert system, fuzzy logic.

АНАЛИЗ ЭФФЕКТИВНОСТИ СМЕШАННОГО ТОРМОЖЕНИЯ С УПРАВЛЕНИЕМ МЕТОДОМ НЕЧЕТКОЙ ЛОГИКИ ПРИ РАБОТЕ АНТИБЛОКИРОВОЧНОЙ СИСТЕМЫ ЭЛЕКТРОМОБИЛЯ

The article discusses the process of braking an all-wheel drive electric vehicle with an indi-vidual drive of the driving wheels with a different coefficient of adhesion of tires to the road along the sides of the electric vehicle. A feature of this work is the use of fuzzy logic in the braking torque control system by the actuators of the front axle of the vehicle. Based on the results of the study, it was concluded that the proposed type of control is preferable in comparison with the control of only one actuator - the brake mechanism.

Сучасні технології управління аутсорс ІТ-проєктами

The article is aimed at analyzing modern approaches to management of IT projects and revealing the possibilities for their improvement based on the use of fuzzy logic in the overall decision-making circuit. Based on the analysis of the main rating sites, a conclusion has been made about the powerful development of IT outsourcing in Ukraine. The main international and domestic ratings regarding the Ukrainian IT outsourcing market are analyzed, criteria of success and reasons for rapid development are allocated. Upon analyzing a sufficient number of works and materials, the authors generalized, synthesized and classified the main difficulties that arise in the process of developing a portfolio of IT projects in terms of outsourcing companies. The most common instruments used to overcome these difficulties are analyzed. Focus is set on the Stage-Gate framework as the most appropriate for both adaptation and use in the process of managing the portfolio of outsourcing IT projects. A modification of Stage-Gate framework is proposed, which takes into account peculiarities of managing outsourcing IT projects and uses a fuzzy logic apparatus for decision-making when navigating between stages of the framework. The purpose of the modification is the possibility to create on its basis an expert system to support decision-making in the process of managing the portfolio of projects. This, in turn, leads to an overall increase in the level of project portfolio management. Prospects for further research are the introduction of the expert system into the practice of work of domestic IT outsourcing firms. The parallel direction consists in analyzing the international IT outsourcing market. This will help to increase the efficiency of the proposed modification and provide favorable conditions for the integration of the Ukrainian IT outsourcing market into the international practices.

GEOBIA, TREE DECISION AND HIERARCHICAL CLASSIFICATION FOR MAPPING GULLY EROSION

The gullies provoke environmental, social and financial damages. The application of corrective and preventive measures needs gullies mapping and monitoring. In this scope, this study proposes a methodology for gullies delimitation using object-oriented image analysis. For such, there were used high spatial resolution imagery and ALS data applied for two study areas, one in Uberlandia-Minas Gerais (Brazil) and another one in Queensland (Australia). The objects were generated by multiresolution segmentation. The most important attributes on the delimitation of the gullies were selected using decision tree induction algorithms, being them: spectral, altimetric and texture. Classifications by decision trees and hierarchical were carried out. The use of decision tree allowed the selection of attributes and the establishment of preliminary decision rules. However, since this procedure did not use fuzzy logic, mixtures between classes could not be evidenced in the rule base. Moreover, the classification was performed by a factor of scale only, which did not allow the identification of all the constituent features of the gully. In hierarchical classification, the procedure is performed on different scales, allowing the use of fuzzy logic to describe different degrees of membership in each class, which makes it a very attractive method for cases such as this study, where there is mixing of classes. The classification obtained with hierarchical classification it was more reliable with the field truth, by allowing the use of different scales, uncertainty insert and integration of knowledge, compared to the automatic classification by decision tree.

APPLICATION OF MATRIX APPROACH OF FUZZY LOGIC FOR DECISION SUPPORT IN OIL MINING EQUIPMENT SERVICE

The work solves the problem of building an intelligent decision support system for servicing oil production equipment. At the first stage – the choice of an intelligent model – it is shown that in the existing conditions it is difficult to obtain a training sample in digital form. On the other hand, there is an opportunity to gain knowledge of subject matter experts – masters and technologists – in the form of a set of linguistic rules. Based on this, a conclusion about the effectiveness of the use of fuzzy logic to solve this problem is made. At the stage of constructing an intelligent model, the use of the matrix approach of fuzzy logic is proposed. To elaborate this approach an algorithm of fuzzy inference based on vector fuzzy predicates is developed. Capabilities and advantages of new algorithm are demonstrated. In particular, it is shown that the matrix representation makes possible reducing computations to solving a system of linear equations. Matrix inference also allows to explicitly determine the range of values of the analyzed parameters at which the knowledge base does not allow making a clear conclusion. A model of a fuzzy logic machine in the form of a fuzzy combinational circuit that analyzes an external memory block is proposed for the analysis of retrospective information on the change in the values of the parameters of technological equipment over time. Specific cases allowing the transition from a state machine to a combinational circuit are shown. Article also shows how this can be done. The main advantage of this approach is the absence of the need to use the difficult to formalize concept of a fuzzy state, which leads to a simplified construction of fuzzy logical devices with memory. At the end work contains brief conclusions about the application of the proposed methods and algorithms for building, testing, implementing a decision support system and about its effectiveness.

WITHDRAWN: Gait diagnosis using fuzzy logic with wearable tech for prolonged disorders of diabetic cardiomyopathy

Abstract Gait patterns and coordination are altered in diabetic cardiomyopathy patients with Parkinson's disease. There is little information with possible explanations. Fall identification and simulation in all age groups, including the use of Fuzzy logic and sensor data for evaluating gait error. The current proposal emphasizes about fall prediction and estimation of diabetic cardiomyopathy disorders for aged, adults and infants. The inputs for the fluctuating inference system are the patient's height, gyroscope, age and weight. RMS error, Estimation and identification are the output variables where inputs are from the MIMO-used sensor data. Input and output variables and rules are supplemented with the membership functions. The extracted features are equivalent to ordinary random mean square error values. The application of a fuzzy Mamdani technique uses a triangular-trapezoidal logic to obtain a random mean square error, classification and recognition requirements for the elderly, adults and children. IoT monitors the collected outputs and store in cloud. The accuracy of a study through triangular trapezoidal approaches of 20 patients with Parkinson's diabetes cardiopathy in the fuzzy logic is 95%. A wide range of fields such as digital control, data detection, decision interpretation, expert systems and computer vision have been employed successfully.

From Classical to Fuzzy Databases in a Production Enterprise

This article presents the evolution of databases from classical relational databases to distributed databases and data warehouses to fuzzy databases used in a production enterprise. This paper discusses characteristics of this kind of enterprise. The authors precisely define centralized and distributed databases, data warehouses and fuzzy databases. In the modern global world, many companies change their management strategy from the one based on a centralized database to an approach based on distributed database systems. Growing expectations regarding business intelligence encourage companies to deploy data warehouses. New solutions are sought as the demand for engineers' expertise continues to rise. The requested knowledge can be certain or uncertain. Certain knowledge does not any problems and is easy to obtain. However, uncertain knowledge requires new ways of obtaining, including the use of fuzzy logic. It is from where the fuzzy database approach takes its beginning. The above-mentioned strategies of a production enterprise were described herein as a case of special interest.

Indonesian Language Portfolio in Elementary Schools Based on C++, C# and MySQL Server

The form of programming applications based on C ++ and C # lies in the design of portfolio-based assessment that is more functional and has high flexibility in terms of developing programming directions. The objectives of this study are (1) measuring infrastructure readiness from authentic use digital database -based assessment, (2) measuring software availability and (3) measuring the availability of spare parts for hardware in the implementation of authentic digital based assessment. The method used is big data analysis from various website. The results of this study (1) the application of authentic assessment digitally based, would be implemented if use storage portfolio, (2) for portfolio analysis can use fuzzy logic combined with the programming language C ++ and (3) the use of analytics to the data analysis of the pattern in the portfolio can be used for error analysis in formal grammar. It is suggested to policymakers to redesign the use of fuzzy logic in analytic design for data pattern analysis in student portfolios. The C # based application development for learning management system scoring is part of developing creative writing skills.

ORDERING THE MOST RELEVANT SKILLS IN AN ENGINEERING DEGREE USING FUZZY LOGIC. A CASE STUDY

Nowadays, the increasing uncertainty of a globalized world economy poses additional challenges to the new agricultural engineering graduates. They have to face increasingly complex challenges, such as increasing demand for agricultural produce in a Climate Change situation, a growing difficulty to guarantee food safety caused by global trade, and an improvement of the resilience of productive systems based on precision agriculture. All of this along with the drawback of a reduced interest of new students in this kind of graduate study. Previous works have dealt with the importance of the general skills in an agricultural engineering degree, showing the relevance of the instrumental skills (capacity for analysis and synthesis, organization and planning capacity, ability to manage information, oral and written communication, foreign language knowledge, computer knowledge, problem resolution, and decision making). This work aims to order these instrumental skills to face the above-mentioned challenges in a more effective way. We are aware that the result of this order presents high doses of uncertainty and ambiguity, and that is why we propose the use of fuzzy logic. The application of this methodology based on fuzzy mathematics can contribute to updating the university degrees so that graduates can successfully the new challenges they will encounter in the workplace. Results show that capacity for analysis and synthesis, organization and planning capacity, and foreign language knowledge is the best-considered skills

Finding the Links between Risk Management and Project Success: Evidence from International Development Projects in Colombia

The aim of this research is to help improve the effectiveness of international development projects (IDPs) with a focus on enhancing their success. For this purpose, this work seeks to identify links between the management of risks among five projects executed in Cauca (Colombia) and the success of these projects in terms of project management and impacts on the beneficiary communities. An analysis of these projects reveals the most critical risks encountered and the relationships between the management of those risks and the success of the projects. The use of fuzzy logic through the fuzzy-set qualitative comparative analysis (fsQCA) program is key to performing this difficult task. The results of a qualitative study reveal that the most important risks correspond to economic, cultural, and political factors. A quantitative analysis by fsQCA shows a direct relationship between the management of cultural differences and the positive impacts of IDPs on the beneficiary communities.