Use Of Fuzzy Logic Research Articles

Fuzzy Fourier Approximations for Uncertain Data Theory and Applications

This study explores the integration of fuzzy logic with Fourier series and transforms to address the challenges posed by uncertainty and imprecision in real-world data. By representing uncertain data through fuzzy numbers and applying fuzzy Fourier approximations, this method enhances the accuracy and robustness of signal processing, image reconstruction, and time-series forecasting, particularly in noisy environments. The comparative analysis demonstrates that fuzzy Fourier methods outperform traditional Fourier techniques in handling uncertainty, while recognizing the computational complexity introduced by fuzzification. The study also explores future research directions, including multi-dimensional data processing, hybrid approaches with machine learning, and the use of fuzzy logic in quantum Fourier transforms. These advancements offer promising solutions for improving data analysis in fields like telecommunications, medical imaging, and financial forecasting, where uncertainty is a critical factor.

Use of Fuzzy Logic in Determining the Academic Achievement of Secondary School Students in Mathematics Class

Many studies have been found in the field of education using the fuzzy logic approach. However, no study has been found examining the academic achievement of secondary school students in mathematics. The research data consists of the exam notes of the 1st term 1st and 2nd Mathematics course of the 2023-2024 academic year in Kütahya. The study consists of the exam scores of 876 students studying in 4 secondary schools in the center of Kütahya. To study a fuzzy logic-based two-input and single-output system was designed using Fuzzy Logic Toolbox in the Matlab program. Totally 9 fuzzy rules were created. The academic success was evaluated first with classical method and then with the designed fuzzy-based model. According to the results obtained, it has been observed that there is a high positive relationship between fuzzy logic method and classical logic method. It was academic success score obtained fuzzy logic method was less than classical logic method for grades 5 and 7. But it was obtained fuzzy logic method was more than classical logic method for grades 6 and 8. In conclusion was reached in favor of fuzzy logic due to fuzzy logic method gives more flexible results.

Pompeiu-Hausdorff Fuzzy <i>b</i>-metric Spaces Are Associated with a Common Fixed Point and Multivalued Mappings

The notion of fuzzy logic was introduced by Zadeh. Unlike traditional logic theory, where an element either belongs to the set or does not, in fuzzy logic, the affiliation of the element to the set is expressed as a number from the interval [0, 1]. The study of the theory of fuzzy sets was prompted by the presence of uncertainty as an essential part of real-world problems, leading Zadeh to address the problem of indeterminacy. The theory of a fixed point in fuzzy metric spaces can be viewed in different ways, one of which involves the use of fuzzy logic. Fuzzy metric spaces, which are specific types of topological spaces with pleasing ”geometric” characteristics, possess a number of appealing properties and are commonly used in both pure and applied sciences. Metric spaces and their various generalizations frequently occur in computer science applications. For this reason, a new space called a Pompeiu-Hausdorff fuzzy <i>b</i>-metric space is constructed in this paper. In this space, some new fixed point results are also formulated and proven. Additionally, a general common fixed point theorem for a pair of multi-valued mappings in Pompeiu-Hausdorff fuzzy <i>b</i>-metric spaces is investigated. The findings obtained in fuzzy metric spaces, such as those discussed in Remark 3.1, are generalized by the results in this paper, and additional specific findings are produced and supported by examples. The study of denotational semantics and their applications in control theory using fuzzy <i>b</i>-metric spaces and Pompeiu-Hausdorff fuzzy <i>b</i>-metric spaces will be an important next step.

Analysis of Metaverse Knowledge Levels of Prospective Mathematics: ANFIS Approach

Education is a field that is affected by technological development and requires rapid adaptation. Metaverse is one of these technologies and it is predicted that it will take its place widely in the world of the future, including education in research. However, it is seen that there are few studies on metaverse and the studies are generally analysed using statistical methods. From this point of view, the aim of this study was to predict the metaverse knowledge levels of pre-service mathematics teachers by using Adaptive Neuro-Fuzzy Inference System (ANFIS) and to create models. The use of fuzzy logic has spread to the field of education with the development of science and technology. ANFIS combines neural network research and fuzzy logic to utilise the relevant capabilities. Considering this important advantage, ANFIS model was established to predict the metaverse knowledge levels of pre-service teachers. The research was conducted with the participation of 192 pre-service teachers. Personal information form and metaverse scale were used as data collection tools. As a result of the study, the scores of the pre-service teachers obtained from the metaverse scale were found to be at a moderate level and the real and artificial scores of the pre-service teachers' metaverse knowledge levels were found to be quite close to each other.

Tourism Application Development with a Recommendation System based on Fuzzy Logic

The article addresses issues related to the breadth and multifaceted aspects of the tourism industry. It discusses the practical benefits of using recommendation systems based on mobile devices. Mobile applications with recom­mendation systems play an important role in the tourism planning process, offering users personalized suggestions that enhance their travel experience. The main focus of this work is on the use of fuzzy logic as the basis for the rec­ommendation system, which allows for a more flexible and accurate suggestion mechanism that takes into account uncertainties and the diversity of user preferences. The methodology proposed by the authors is based on applying a fuzzy content model to the described problems as an effective extension of the collaborative model. The article describes a method for implementing the recommendation system algorithm, lists the technical tools used to imple­ment the application, presents an activity diagram of the working application, describes the process of searching, preparing, and annotating data used in the working application. The presented software and methodological tools help to solve problems associated with the clear categorization of tourist objects within traditional types of tourism, arising from the broad classification of tourist objects, thereby improving the quality of the tourist experience.

Модель процесса формирования каталога сценариев для анализа и оценки функциональной безопасности автоматизированных систем вождения автомобилей

In the development of highly automated and unmanned vehicles, the auto industry is faced with a new set of problems associated with the need to standardize elements of automated driving systems and determine the rules for their testing and certification. Due to the acceleration of scientific and technological progress and the development of new technologies, previously used standards began to become obsolete as the practice of their application accumulated, and sometimes even before the approval of a particular technical regulation. In these conditions, a promising technical and legal solution may be the use of fuzzy logic in tools for aggregation and management of expert knowledge in the processes of certification and testing of highly automated vehicles and elements of their automated control systems. Fuzzy decision-making models use typical fuzzy situations that form catalogs of scenarios for testing vehicles and their systems. In this way, an expert system knowledge base can be formed in which knowledge engineers apply a set of test scenario or experiment parameters for testing and simulation. Determining the parameters of a new scenario, their similarity with previously formalized scenarios and the decision to include a particular scenario in the scenario catalog remains with the experts. The approaches used at the present stage to the formalization of expert knowledge are not acceptable for creating knowledge bases controlled by big data management systems or artificial neural networks, which are in the near future in the development of expert systems. The article proposes a method by which a knowledge engineer administering an expert system can automate the creation of a catalog of “fuzzy” test scenarios and simulation modeling of systems, by automatically searching for the maximum value of the states of these systems belonging to maps and catalogs of scenarios, with a probability specified by the expert, with using machine learning methods.

Towards Refined Autism Screening: A Fuzzy Logic Approach with a Focus on Subtle Diagnostic Challenges

This study explores the creation and testing of a Fuzzy Inferencing System for automating preliminary referrals for autism diagnosis, utilizing membership functions aligned with the Autism Quotient 10-item questionnaire. Validated across three distinct datasets, the system demonstrated perfect accuracy in deterministic settings and an overall accuracy of 92.91% in a broad fuzzy dataset. The use of Fuzzy Logic reflects the complex and variable nature of autism diagnosis, suggesting its potential applicability in this field. While the system effectively categorized clear referral and non-referral scenarios, it faced challenges in accurately identifying cases requiring a second opinion. These results indicate the need for further refinement to enhance the efficiency and accuracy of preliminary autism screenings, pointing to future avenues for improving the system’s performance. The motivation behind this study is to address the diagnostic gap for high-functioning adults whose symptoms present in a more neurotypical manner. Many current deep learning approaches for diagnosing autism focus on quantitative datasets like fMRI and facial expressions, often overlooking behavioral traits. However, autism diagnosis still heavily relies on long histories and multi-stakeholder information from parents, teachers, doctors and behavioral experts. This research addresses the challenge of creating an automated system that can handle the nuances and variability inherent in ASD symptoms. The theoretical innovation lies in the novel application of Fuzzy Logic to interpret these subtle diagnostic indicators, providing a more systematic approach compared to traditional methods. By bridging the gap between subjective clinical evaluations and objective computational techniques, this study aims to enhance the preliminary screening process for ASD.

Fuzzy Control Systems for Power Quality Improvement—A Systematic Review Exploring Their Efficacy and Efficiency

Fuzzy-based control systems have demonstrated a remarkable ability to control nonlinear processes, a characteristic commonly observed in power systems, particularly in the context of power quality enhancement. Despite this, an updated and comprehensive literature review on the applications of fuzzy logic in the domain of power quality control has been lacking. To address this gap, this study critically examines published research on the effective and efficient use of fuzzy logic in resolving quality issues within power systems. Data sources included the Web of Science and academic journal databases, followed by an evaluation of target articles based on predefined criteria. The information was then classified into seven categories, including control system type, features of the fuzzy logic controller, fuzzy logic inference strategy, power quality issue, control device, implementation methodology (efficacy testing), and efficiency improvement. Our study revealed that fuzzy-based control systems have evolved from simple type-1 fuzzy controllers to advanced control systems (type-2 fuzzy and hybrid) capable of effectively addressing complex power quality issues. We believe that the insights gained from this study will be useful to both experienced and inexperienced researchers and industry engineers seeking to leverage fuzzy logic to enhance power quality control.

Использование нечеткой логики в системе управления технологическим комплексом напыления покрытий летательных аппаратов

The reliability of aircraft is ensured by various functional coatings applied to them. The quality indicators of coatings are determined by the capabilities of the control system for the technological complex of their spraying. The control system is effective if, with a large number of input parameters, it adopts the optimal technological solution to achieve the set values of coating quality indicators. The use of fuzzy logic in the control system of the technological complex of coating spraying is proposed. Using the Fuzzy Logic Toolbox package of the MATLAB program, an analysis of the fuzzy logic-based control system for the technological operation of applying a sprayed composition to a coated surface was carried out. It is shown that the use of fuzzy logic as an element of artificial intelligence is effective in the control system of the technological spraying process.

Implementation of a fuzzy inference system to support the evaluation of sphygmomanometers for river inspection activities in the area of legal, scientific and compliance metrology

The Amazon region has characteristics that point to logistical difficulties in meeting the demands whose inspection goal is to contribute to the economic development of the craft industry and commerce in the interior of the state, making regional products competitive, by providing technical metrological advice on procedures for verifying weighing and measuring instruments, using river vessels equipped with laboratories for testing pre-measured products, service rooms, IT and training. The Management Model for Basic River Units (UBF), aimed at carrying out inspection activities in the area of legal and scientific metrology and conformity assessment using fuzzy logic for decision-making, proposes an innovative management system for river units, focused on IPEM-AM's inspection activities using INMETRO's methodology. The approach uses fuzzy logic to improve decision-making, making it more accurate and efficient. The aim of the research is to evaluate a pressure device (sphygmomanometer) in order to fulfil inspection activities in the area of legal and scientific metrology and compliance, using fuzzy inference to support decision-making. The methodology used aims to improve the efficiency and effectiveness of inspection activities in these areas, reducing errors and optimising resources. Fuzzy logic is a suitable tool for dealing with the uncertainty and imprecision present in this context, allowing the system to make decisions that are closer to reality in accordance with the legislation. The results of the proposed model can be applied to different types of river units, helping to improve inspection processes and ensuring compliance with current rules and regulations. In addition, the use of fuzzy logic can provide more intelligent management that can be adapted to the changing conditions of the river environment according to the logistical purposes of the region.

Application of Fuzzy Logic to Predict Rice Production Quantity in Bogor Regency

This study aims to understand prediction methods using fuzzy Mamdani logic to conclude the quantity of rice production in the Bogor Regency area. Then in this study, we used quantitative methods and involved interviews with resource persons, namely rice mill workers and farmers. The data obtained from the interview is then fuzzyfication to be used in calculations. The main finding of this study is the elaboration of crop quantity from small, normal, and many, with the range of yields produced ranging from 0 to 3000 KG for little, 3000 to 6000 KG for normal, and 6000 to 10000 KG for many. Weather and humidity observations were found to significantly affect rice yields. The implications of these findings are improved accuracy in yield estimation, and the ability to address uncertainty and complexity in factors affecting crop growth. Despite the limitations of this study, such as limited data and remote study locations, the use of fuzzy logic can make a crucial contribution to determining the quantity of water needed for plant growth based on rainfall and weather conditions. This research is crucial because it can be a reference for future research that uses fuzzy logic to determine the quantity of rice production.

Use of fuzzy logic for analyzing the socio-economic well-being of the population

The article reveals a new approach to the analysis of population welfare using the theory of fuzzy sets. The purpose of the study is to develop and apply a methodology based on fuzzy logic to quantify and analyze the social and economic well-being of citizens. The main task includes the development of a methodological approach, the construction of a model for assessing well-being and the development of recommendations for planning socio-economic strategies. As a solution, the use of fuzzy logic is proposed, which allows better consideration of ambiguities and uncertainties in the data on the well-being of the population. The study presents a detailed description of the developed model, which includes the formation of lin-guistic variables, the definition of term sets, and the development of inference rules. These components allow the model to adapt to different conditions and types of data, providing flexibility and a wide field of application of the method. The study also emphasizes the impor-tance of analyzing factors that affect the well-being of the population, such as household in-come, expenditure on medicine and food, education level, housing conditions, the importance of personal economy and others. The authors indicate the dynamic influence of these factors and the need for their regular updating to increase the accuracy of forecasts. The results of the study demonstrate that the developed model provides more accurate and reasonable fore-casts of the level of socio-economic well-being compared to traditional methods. The ob-tained data can be used to plan socio-economic strategies at the micro and macro levels, con-tributing to the development of effective measures to improve the well-being of the population. The article makes a significant contribution to the development of socio-economic welfare analysis methods, showing the potential of fuzzy logic as a tool for increasing the accuracy of forecasting and developing sound socio-economic policies.

ENTERPRISE SOFTWARE SELECTION BASED ON Z-AHP METHOD OF DECISION MAKING

ENTERPRISE SOFTWARE SELECTION BASED ON Z-AHP METHOD OF DECISION MAKING

Tapping into long term value: a comprehensive overview of BIST 30 index companies future potential

Purpose - This article aims to establish a correlation between the valuation potential of companies listed on the Borsa Istanbul 30 index and their enduring competitive advantage, focusing on microeconomic Key Performance Indicators (KPIs) and corporate governance dimensions. Methodology - The study conducted an extensive analysis of the financial statements of BIST 30 companies spanning three years. Employing a unique approach utilizing Triangular Spherical Fuzzy Sets, it assessed the significance of these KPIs and corporate dimensions, chosen for their capacity to handle imprecise data. Three experts were consulted over three years to assign importance scores to each indicator, which were then used to calculate cumulative scores for individual stocks. Findings - The application of the Triangular Spherical Fuzzy Sets method provided a comprehensive understanding of these indicators, ena-bling a nuanced evaluation of the cumulative score for each stock. Notably, a clear distinction emerged between service and manufacturing companies, suggesting a potential glass ceiling effect that favors production-oriented enterprises. The observed patterns in certain stocks aligned with the framework developed in this study. Conclusion - This study illustrates the use of fuzzy logic in evaluating stock valuation potential, revealing insights into their sustained growth prospects. Aligned with Warren Buffett's value investing principles, it advances modeling approaches within the Turkish public stock exchange. Offering a unique perspective on BIST 30 stocks' long-term valuation potential, it provides valuable insights for investors, stakeholders, and analysts in the dynamic finance landscape. Emphasizing the understanding of companies' enduring competitive strength in an evolving market is pivotal. Keywords: Value investing, triangular spherical fuzzy sets, long term competitive advantage, corporate governance.

Development of intelligent protection and automation control systems using fuzzy logic elements

In this article, the causes of technological disturbances in electrical systems are considered, and several characteristic disadvantages of the protection and automation of elements of electrical systems are highlighted. The tendency to decrease the reliability of relay protection associated with the transition from analog to digital types of protection is substantiated. Based on the studied examples, the use of fuzzy logic in protections, the expediency of using fuzzy logic elements in protection devices, and the automation of electrical systems to identify types of short circuits are justified. This article analyzes the most common damages and presents the results of modeling an electrical system with transformer coupling, where all types of asymmetric short circuits were initiated. The dynamics of changes in the symmetrical components of short-circuit currents of the forward, reverse, and zero sequences are determined. Rules have been created for the identification of asymmetric types of short circuits. An algorithm of protection and automation operation using fuzzy logic elements has been developed. The proposed algorithm of protection and automation will reduce the time to determine the type of damage and trigger protections.

An Application of Fuzzy Symbolic Time-Series for Energy Demand Forecasting

In this paper, we present a new fuzzy symbolization technique for energy load forecasting with neural networks, FPLS-Sym. Symbolization techniques transform a numerical time series into a smaller string of symbols, providing a high-level representation of time series by combining segmentation, aggregation and discretization. The dimensional reduction obtained with symbolization can speed up substantially the time required to train neural networks, however, it can also lead to considerable information losses that could lead to a less accurate forecast. FPLS-Sym introduces the use of fuzzy logic in the discretization process, maintaining more information about each segment of the neural network at the expense of requiring more space in memory. Extensive experimentation was made to evaluate FPLS-Sym with various neural-network-based models, including different neural network architectures and activation functions. The evaluation was done with energy demand data from Spain taken from 2009 to 2019. Results show that FPLS-Sym provides better quality metrics than other symbolization techniques and outperforms the use of the standard numerical time series representation in both quality metrics and training time.

BHARAT: A simple and effective multi-criteria decision-making method that does not need fuzzy logic, Part-1: Multi-attribute decision-making applications in the industrial environment

A simple and effective multi-criteria decision-making methodology named as “Best Holistic Adaptable Ranking of Attributes Technique (BHARAT)” is proposed that can be used in single- as well as group decision-making scenarios of the industrial environment. The attributes data for various alternatives can be quantitative or qualitative (i.e., expressed in linguistic terms). This paper proposes to transform the qualitative attributes into quantitative attributes by means of simple linear scales rather than complex fuzzy scales. The proposed BHARAT method normalizes the data with reference to the “best” alternative corresponding to an attribute and the normalization procedure is repeated for all the attributes to get the normalized data. A group of decision-makers or a decision-maker assigns ranks to the attributes according to how important they are deemed to be, and these ranks are then transformed into the proper weights. The total scores of the alternatives are calculated by multiplying the weights of the attributes by the corresponding normalized data of the attributes for different alternatives. Four industrial case studies are presented to illustrate the potential of the suggested BHARAT method. The first case study deals with the problem of an automated warehouse selection for a large industrial plant involving a single decision-maker, 13 attributes, and 4 alternative warehouses; the second case study deals with the problem of sustainable maintenance service provider selection for a large petrochemical plant involving fuzzy group decision-making with 5 decision-makers, 9 attributes, and 4 alternative maintenance service providers; the third case study deals with the problem of alternative strategy selection for implementation of a make-to-order system for passenger car manufacturers involving 6 factors, 18 sub-factors, and 3 alternative strategies; and the fourth case study deals with the problem of process parameters selection in a sustainable high speed turning operation involving 4 attributes and 9 alternative sets of experimental conditions. The results of the proposed decision-making method and its second version are compared with the other popular decision-making methods. The proposed method and its another version are proved simple, effective, powerful, flexible, easy to apply, do not require the use of fuzzy logic, offer logical and consistent procedures to assign weights to the attributes, and are applicable to different decision-making scenarios of the industries. Part-1 of this paper describes the applications of the BHARAT method to multi-attribute decision-making problems and Part-2 describes the evaluation of Pareto solutions using the BHARAT method in multiple objective decision-making problems.

Fuzzy Logic as a Decision-Making Tool for Transport Request Selection

In the competitive transport industry, carriers strive to deliver quality service while meeting the diverse needs of their customers and their own unique characteristics. Few rail freight operators use the tools available to help them make the right decisions about what is profitable and what is not. Evaluating individual transport requests and making the right choices can lead to better service and improved profitability. To efficiently evaluate individual transport requests and make informed decisions, this paper proposes the use of fuzzy logic can be used. The paper considers 6 real transport requests and evaluates them using fuzzy logic.

A Fuzzy Inference-Based TCSC Control Technique to Improve Dynamic Power System Responses

With the expansion of transmission systems, the devices that contribute to the overall power system performance must be adequate to the increasing modeling complexity and requirements. In this sense, the Flexible AC transmission system (FACTS) devices are often employed to improve stability and power quality, while the Thyristor-Controlled Series Compensator (TCSC) is a common example, able to change the equivalent transmission line impedance, improving power flow. This work discusses two established techniques for the control of TCSC devices based on the lead-lag model, whose parameters are defined through metaheuristic techniques, such as Genetic Algorithm and Particle Swarm Optimization. The results found in these publications are implemented in the Matlab/Simulink environment and confronted with the proposed Fuzzy logic application, written with linguistic inference, simplified rules, and simple membership functions. The two published models and the proposed Fuzzy logic performed satisfactorily with very similar results in all scenarios simulated considering a Single Machine Infinite Bus (SMIB) equivalent system model. The big advantage of the use of Fuzzy logic is its modeling simplicity, unlike the heuristic techniques that require much more modeling time and, sometimes, a large number of iterations to achieve acceptable parameters.

Facilitating Communication in Neuromuscular Diseases: An Adaptive Approach with Fuzzy Logic and Machine Learning in Augmentative and Alternative Communication Systems

Augmentative and alternative communication techniques (AAC) are essential to assist individuals facing communication difficulties. (1) Background: It is acknowledged that dynamic solutions that adjust to the changing needs of patients are necessary in the context of neuromuscular diseases. (2) Methods: In order address this concern, a differential approach was suggested that entailed the prior identification of the disease state. This approach employs fuzzy logic to ascertain the disease stage by analyzing intuitive patterns; it is contrasted with two intelligent systems. (3) Results: The results indicate that the AAC system’s adaptability enhances with the progression of the disease’s phases, thereby ensuring its utility throughout the lifespan of the individual. Although the adaptive AAC system exhibits signs of improvement, an expanded assessment involving a greater number of patients is required. (4) Conclusions: Qualitative assessments of comparative studies shed light on the difficulties associated with enhancing accuracy and adaptability. This research highlights the significance of investigating the use of fuzzy logic or artificial intelligence methods in order to solve the issue of symptom variability in disease staging.