Fuzzy System Theory Research Articles

Practical Teaching Mode Structure for Ideological and Political Theory Course Based on Fuzzy System Theory

The application of graph theory is instrumental in the construction of a practical teaching mode structure for an ideological and political theory (IPE) course rooted in fuzzy system theory. In recent years, IPE education has received more and more attention. System theory can well construct the IPE teaching mode, so we need to have a good grasp of system theory. This paper starts with the significance of system theory in the practical teaching of IPE, finds the IPE curriculum that conforms to system theory, and constructs the basic mode of practical teaching of IPE on this basis. Using the idea of a multi-level fuzzy comprehensive evaluation to quantify teachers’ teaching quality, this paper establishes an algorithm model to quantitatively measure teachers’ teaching quality fuzzy.

Refining Environmental Sustainability Governance Reports through Fuzzy Systems Evaluation and Scoring

Environmental, Social, and Governance (ESG) reports have become essential tools for enterprises to showcase their commitment to sustainable development and social responsibility. However, discrepancies persist regarding the criteria, assessments, and ratings disclosed in these reports. Moreover, there is a need for more objective methods to determine the weight distribution of indicator items. This study introduces a novel approach utilizing semantic variables in fuzzy theory and a multiple logic fuzzy inference system to develop an ESG environmental management performance assessment model. Therefore, this paper aims to develop a novel approach utilizing semantic variables and a multiple logic fuzzy inference system to quantitatively evaluate the sustainable performance of an environmental management plan. This research also aims to ensure fair and objective assessment outcomes, providing valuable guidance for enterprises in implementing performance management strategies. Key aspects investigated include the impact of membership functions, the extended utilization of semantic variables and logical rules, a comparative analysis of traditional weight assessments, and the limitations of applying fuzzy theory. Through comprehensive discussions and calculations, it is evident that fuzzy theory offers considerable flexibility in application. By tailoring fuzzy rules and selecting appropriate membership functions, diverse application scenarios can be accommodated. The Fuzzy systems evaluation and scoring EMP model generates EMP evaluation scores ranging from 1.76 to 8.29 for Gaussian membership, 1.80 to 8.19 for Triangular membership-A, 1.92 to 8.00 for Triangular membership-B, and 1.81 to 8.19 for Quadrilateral trapezoidal membership, based on simulated rating scenarios using the semantic variables of completeness and feasibility. This approach successfully incorporates distribution logic from subjective membership degrees to evaluate EMP scores. The findings demonstrate that fuzzy theory enables the consideration of multiple factors and facilitates the provision of objective-level membership, underscoring its potential in addressing complex evaluation challenges. This study illuminates the versatility of the fuzzy system theory, with its applications poised to extend across various domains.

Application of an approach integrating the Kano model and the fuzzy systems theory: a strategic analysis of consumer satisfaction scores

PurposeThe aim of this research is to propose a quantitative approach to evaluating the quality of services provided, helping organizations to make strategic decisions by better understanding the characteristics that satisfy consumers.Design/methodology/approachThe approach was based on the integration of the Kano model with SERVQUAL, adapted by the satisfaction equations of Albuquerque et al. (2022) and fuzzy systems theory. Through this, it was possible to infer which attributes influence customer satisfaction, identifying the ranges of satisfaction and, with the help of fuzzy, reducing the imprecision of customer perceptions.FindingsA total of 42% of the attributes were classified as unidimensional, with attribute 11 (Reliability) and attribute 9 (Courtesy) having the highest satisfaction values. Attractive attributes accounted for 38% of the sample, with attribute 29 (Variety of products) and attribute 7 (Location) having the greatest impact on satisfaction. On the other hand, attribute 30 (Order Delay) and attribute 31 (Waiting for payment) caused more dissatisfaction among consumers (ranges −0.6, −0.71, respectively). In addition, Variety of products was the most satisfactory, while Order Delay generated the most dissatisfaction among users.Originality/valueThe originality of this research lies in its contribution to organizations in relation to the services offered by investigating a gap in the studies that use the Kano model, integrated with SERVQUAL, which do not include reverse attributes in their equations and analyses. With the help of fuzzy sets, the subjectivity of the individual can be translated into data for greater clarity of information.

Editorial for Thematic Section: Theory and Applications of Fuzzy Systems, Neural Networks, and Metaheuristics

Editorial for Thematic Section: Theory and Applications of Fuzzy Systems, Neural Networks, and Metaheuristics

The method of assessing the effectiveness of the security of confidential data of the distributed information system

The paper proposes a method of assessing the effectiveness of the security of confidential data of a distributed information system, based on a model for determining the current threats to the security of confidential data in the information system, on algorithms of fuzzy inference and the theory of fuzzy neural systems, unlike known ones, it uses sufficient and necessary indicators, excludes expert errors , increases the detection of actual threats to the security of confidential information system data by 5%, reduces the cost of purchasing information protection tools from 15 to 30%. It takes into account the following factors: the IT infrastructure of the distributed information system, the capabilities of attackers and their level of motivation in the information system. The proposed approach differs from existing ones by an automated process, the need to involve highly qualified specialists in the field of information security, and low computational complexity; absence of deficiencies in expert assessments; allows you to determine the list of current information security threats in information systems of various classes and types. The task of ensuring the security of confidential data is urgent, which is due to the growth of computer attacks and leaks of information, which are reflected in the statistical data on the commission of crimes in the field of high technologies, the growth of criminal activity using modern communication devices and the Internet. The existing methods of identifying current threats to information security and assessing the effectiveness of the security of confidential data cannot be used at all stages of the life cycle of distributed information systems, they do not take into account the following indicators in the complex: IT infrastructure of distributed systems, current threats to information security, security requirements of confidential data, their cost as important indicators when solving these problems. One of the most important tasks of ensuring the security of confidential data is the evaluation of the effectiveness of the protection system. In this regard, the goal of the research is to improve the quality of the assessment of the effectiveness of the security of confidential data of a distributed information system by determining sufficient and necessary indicators using modern information technologies, which allow the most effective solution of the following tasks: determining the parameters of the adaptive production fuzzy neural systems, which most suitable for solving the tasks, the application of Data Science technologies in data processing, algorithms of fuzzy output.

Model of current threats to security of confiden-tial data in divisional information systems

The paper proposes a model for determining actual threats to the information security of distributed information systems, based on algorithms of fuzzy inference and the theory of fuzzy neural systems, unlike known ones, uses sufficient and necessary indicators, excludes expert errors, increases the detection of the number of actual threats to information security of distributed systems by 5 %, reduces the cost of purchasing information protection equipment from 15 to 30%. It takes into account the following factors: the IT infrastructure of the distributed information system, the capabilities of attackers and their level of motivation in the distributed information system, the list of existing protection tools in the distributed information system.
 The proposed approach differs from existing ones in the following: lack of involvement of highly qualified specialists in the field of information security; the process is automated, has a low computational complexity; absence of deficiencies in expert assessments; allows you to determine the list of current information security threats in information systems of various classes and types.
 The task of ensuring the security of confidential information becomes the most urgent, which is due to the growth of computer attacks and leaks of information, which are reflected in the statistical data on the commission of crimes in the field of high technologies, the growth of criminal activity using modern communication devices and the Internet.
 Existing methods of modeling current threats to information security and assessing the effectiveness of the information protection system cannot be used at all stages of the life cycle of distributed information systems - they do not take into account the following indicators in the complex: IT infrastructure of distributed information systems, current threats to information security, security requirements for confidential information, a list of means of protecting confidential information and their value as important indicators when solving these problems.
 In order to achieve the goals of ensuring the security of confidential information, it is necessary to: organize the effective creation of an information protection system (information security system), effective modeling (identification of the list) of current threats to information security, identification of the current violator, and also provide the opportunity to conduct a qualitative assessment of the effectiveness of the information security (protection) system.
 One of the most important tasks of ensuring the security of confidential information is the assessment of the effectiveness of the protection (security) system. In this regard, the goal of the work (research) is to improve the quality of the evaluation of the effectiveness of the protection (security) systems of distributed information systems by determining sufficient and necessary evaluation indicators using modern (promising) information technologies that allow the most effective solution of the following tasks: determination operating parameters of adaptive production fuzzy neural systems, which are most suitable for solving the tasks, application of Data Science technologies in data processing, fuzzy output algorithms.

Adaptive fixed‐time control for nonlinear systems subject to mismatched uncertainties and external disturbance: An ESO‐based strategy

AbstractIn this paper, the tracking problem is concerned for a class of nonlinear system. The primary objective is to achieve fixed‐time control performance in the presence of nonvanishing mismatched uncertainties and nonparametric external disturbance. Firstly, in order to estimate disturbance, a fixed‐time extended state observer with fractional power term is developed, the order of which is reduced to alleviate peaking value phenomenon. By reasonably switching among different estimation patterns, both transient and steady‐state performances are balanced. Secondly, by means of fuzzy logic system theory, an ESO‐based adaptive fuzzy control strategy with time‐varying gains is presented, and thus the mismatched nonlinearities are approximated, and the external disturbance is compensated in real time simultaneously. Moreover, by constructing a mixed barrier and quadratic type of Lyapunov function, the fixed‐time stability analysis is put forward for tracking error dynamics based on backstepping methodology. Finally, the advantages and effectiveness are illustrated by a numerical example on wheeled mobile robot. The main characteristic of this paper is to integrate ESO methodology into fuzzy control architecture in fixed‐time sense. As a consequence, not only the output tracking purpose, but also desirable transient performance can be achieved.

METHODOLOGICAL APPROACHES TO ASSESSING THE LEVEL OF ECONOMIC SECURITY OF THE ENTERPRISE

The article is devoted to the problem of assessing the level of economic security of enterprises in modern conditions. The authors define economic security as an important aspect of the functioning and development of enterprises in a dynamic economic environment. The authors point out the urgency of the problem of finding criteria and tools for assessing this indicator, given the variety of approaches to determining the economic security of an enterprise in the literature. The methodology uses general scientific and special methods, such as analysis, synthesis, logical, terminological, financial, systemic and structural analysis, systematization and generalization, to confirm the reliability of the results obtained. The approaches and methods for assessing the level of economic security of an enterprise depend on the diversity of interpretations of the very concept of «economic security of an enterprise», which leads to the absence of a single indicator for a full assessment of this phenomenon. Different approaches to assessing the economic security of an enterprise are presented through resource-functional, indicator, profit and investment, three-dimensional, program-target, bankruptcy forecasting, economic and mathematical, systemic and economic risk theory. Each of these approaches uses different methods and criteria to determine the level of economic security of an enterprise. It is noted that economic security assessment can be carried out using various methods, such as expert assessments, regression and variance analysis, exponential smoothing, fuzzy systems theory and multivariate statistical analysis. The author emphasizes the importance of a systematic approach that takes into account internal and external relations, interdependence and subordination of the functional components of an enterprise when assessing the level of its economic security. For a comparative analysis of the levels of functional components of the economic security of enterprise, the authors use the calculation of the rates of change of indicators. The general methodology for calculating indicators of the levels of functional components includes analysis and evaluation of the dynamics of indicators, which indicates trends and changes in various areas of economic security of enterprise.

Идентификация локально-адаптивных регрессионных моделей с треугольными индикаторными функциями

The basic idea of constructing locally-adaptive regression models (LAR models) consists in the use of regressors defined on the local subregions of factor values. The belonging of factor values to a particular local subdomain is set by indicator functions. Indicator functions by their nature are close to the well-known concepts of membership functions from the theory of fuzzy systems (Fuzzy Systems). As a rule, to provide the required smoothness of the required dependence of the response on the acting factors such local subdomains are defined with overlapping - in the form of the so-called fuzzy partitions. Type or type of indicator functions may be very different: triangular, trapezoidal, and non-linear. Specifying one or another type of indicator function determines the scheme of weighing local models. Each indicator function must be defined for the entire range of the corresponding factor. Triangular-type functions are used as indicator functions in this work. Linear factor models are considered as local models. It is noted that in their original form the proposed LAR models are not identifiable. The issue of identification of such models in the case of joint estimation of all parameters is considered. The procedure of model reduction is introduced. The resulting model is written out in the space of functions that allow estimation. In the case of dividing the domain of factor determination into two, three or four fuzzy partitions we propose the basis of functions allowing evaluation. The results of computational experiment on regression dependence reconstruction by ordinary polynomials of different degrees and by LAR models are given. The efficiency of LAR models in comparison with polynomials of degree 3 and 4 is noted.

GDASC: Practical teaching mode structure for IPE course based on fuzzy system theory

GDASC: Practical teaching mode structure for IPE course based on fuzzy system theory

Acousto-optic Multi-source Information Fusion Algorithm for Outdoor Pedestrian Detection

The inefficiency of traditional rescue methods is a serious threat to the life and health of outdoor people in distress, so the research on pedestrian identification for outdoor rescue has important theoretical significance and application value. In order to solve the problem of low probability of recognition based on signal information in traditional methods, an outdoor pedestrian recognition model based on acousto-optic information fusion is constructed. Considering the characteristics of the information source, the directional gradient histogram of the infrared image and the Mel Cepstral Coefficients of the acoustic signal are selected as the factors affecting the recognition probability, and the pedestrian recognition probability correlation equation is deduced; Based on the improved D-S evidence theory of fuzzy system, a pedestrian recognition method based on infrared image and sound signal is proposed, and the mapping relationship between two kinds of information sources and detection probability is established to improve the pedestrian recognition probability. The hardware-in-the-loop simulation results show that the pedestrian recognition probability of fusion of infrared image and sound signal is higher than that of single information, which provides an efficient and reliable method for outdoor rescue.

Event-Based Fuzzy Tracking Control for Nonlinear Networked Systems Subject to Dynamic Quantization

This paper investigates the problem of event-based output feedback tracking control for discrete-time nonlinear networked systems with dynamic quantization. The Takagi-Sugeno (T-S) fuzzy systems theory is utilized to approximate the investigated nonlinear systems. Three general dynamic quantizers and an improved asynchronous event-triggering communication scheme are carried out to comprehensively decrease the amount of data in the communication of network and realize the rational utilization of limited communication resources. The objective is to design an event-based static output feedback tracking controller such that, in the presence of dynamic quantization, the closed-loop system can be asymptotically stabilized, and the tracking error achieves the predefined tracking performance. Moreover, the parameters for the desired dynamic quantizers and tracking controller can be obtained simultaneously by solving a set of linear matrix inequalities. Finally, the simulation responses are provided to demonstrate the validity of the proposed tracking control strategy.

Electric vehicle charging pile capacity planning based on normal distribution Monte Carlo sampling model

Abstract In recent years, environmental issues have gradually become one of the most concerning issues in the world. New energy vehicles have attracted wide attention by their good environmental and social benefits such as zero exhaust emissions and low noise pollution. This paper proposes a regional charging demand forecasting method for electric vehicles (EVs) based on hierarchical charging decision model to solve the problem of charging pile capacity planning, which affects the charging decision behavior of EVs users. The Monte Carlo sampling model is adopted to obtain the time distribution of electric vehicle arriving at each functional area, the distribution of battery state of charge when arriving, and the distribution of average daily mileage. The negative exponential distribution model is used to obtain the average mileage distribution. The fuzzy system theory is proposed to calculate the charging probability, and the fuzzy rule base is adjusted according to the actual data to ensure that the fuzzy rule base is real and effective. Finally, the charging demand distribution curve of electric vehicles in each functional area is obtained by sampling simulation. Through experimental analysis, optimizing the capacity configuration of charging facilities in the station can well describe how electric vehicle users make charging decisions in the actual situation. The research on electric vehicle charging stations can slow down the waiting time of electric vehicle charging, and then affect the process of replacing fuel vehicles with electric vehicles to achieve the development goal of popularizing electric vehicles. It is of great significance for the promotion of new energy vehicles to rationally plan the location and capacity of electric vehicle charging stations.

EVACUATION ROUTE PLANNING MODEL BASED ON THE FUZZY ANT COLONY OPTIMIZATION ALGORITHM

The construction of evacuation routes from urban areas has become an urgent task in the modern world, as the number of emergencies is constantly increasing. Standard route planning algorithms do not meet the requirements of fast and efficient evacuation because they do not fully consider the environmental parameters and have a high computational complexity, and erroneous results can have critical consequences, including loss of life. The paper proposes a model of finding an optimal evacuation route in emergencies in urban areas based on a modified ant colony optimization algorithm: an ant (a person or a vehicle) is allowed to start moving from several possible vertices of the graph, as well as to end the route at several available vertices. It is connected with the fact that there are usually multiple evacuation start points and destinations. The transitions matrix is built using additional parameters, the uncertainty of which is taken into account using the methods of computing with words and the theory of type-2 fuzzy sets and systems. For modeling, in the paper such additional parameters as the quality of the road surface, the number of road lanes, the level of traffic jams, and the distance to the epicenter of the emergency were used. The proposed model was implemented and applied in one of the quarter of Kyiv.

Regularization scheme for uncertain fuzzy differential equations: Analysis of solutions

<abstract><p>In this paper a regularization scheme for a family of uncertain fuzzy systems of differential equations with respect to the uncertain parameters is introduced. Important fundamental properties of the solutions are discussed on the basis of the established technique and new results are proposed. More precisely, existence and uniqueness criteria of solutions of the regularized equations are established. In addition, an estimation is proposed for the distance between two solutions. Our results contribute to the progress in the area of the theory of fuzzy systems of differential equations and extend the existing results to the uncertain case. The proposed results also open the horizon for generalizations including equations with delays and some modifications of the Lyapunov theory. In addition, the opportunities for applications of such results to the design of efficient fuzzy controllers are numerous.</p></abstract>

Adaptive Fuzzy Command Filtered Finite-Time Tracking Control for Uncertain Nonlinear Multi-Agent Systems with Unknown Input Saturation and Unknown Control Directions

This paper investigates the finite-time consensus tracking control problem of uncertain nonlinear multi-agent systems with unknown input saturation and unknown control directions. An adaptive fuzzy finite-time consensus control law is proposed by combining the fuzzy logic system, command filter, and finite-time control theory. Using the fuzzy logic systems, the uncertain nonlinear dynamics are approximated. Considering the command filter and backstepping control technique, the problem of the so-called “explosion of complexity” in the design of virtual control laws and adaptive updating laws is avoided. Meanwhile, the Nussbaum gain function method is applied to handle the unknown control directions and unknown input saturation problems. Based on the finite-time control theory and Lyapunov stability theory, it was found that all signals in the closed-loop system remained semi-global practical finite-time stable, and the tracking error could converge to a sufficiently small neighborhood of the origin in the finite time. In the end, simulation results were provided to verify the validity of the designed control law.

The stability of the controlled problem of fuzzy dynamic systems involving the random-order Caputo fractional derivative

This article discusses the stability theory of fuzzy fractional dynamic systems with the random-order Caputo fractional derivative (RO-FFDSs). We establish new inequalities on the random-order Caputo fractional derivative, which is an essential tool in investigating the stability theory of RO-FFDSs. Based on the extension of the Lyapunov direct method (LDM), we investigate the asymptotical stability (AS) and the Mittag–Leffler stability (MLS) of the controlled problem of RO-FFDSs via a linear feedback controller. Finally, numerical examples are given to show the effectiveness of the theoretical results.

Using Fuzzy Inference to Evaluation Suppliers in Diyala General Electric Industries Company

The research aims to evaluate the suppliers at Diyala general electric industries company conducted in an environment of uncertainty and fuzzy where there is no particular system followed by the company, and also aims to use the problem of traveling salesman problem in the process of transporting raw materials from suppliers to the company in a fuzzy environment. Therefore, a system based on mathematical methods and quantity was developed to evaluate the suppliers. Fuzzy inference system (FIS) and fuzzy set theory were used to solve this problem through (Matlab) and the problem of the traveling salesman in two stages was also solved by the first stage of eliminating the fuzzing of the environment using the rank function method, while the second stage used the method of the nearest neighbor to solve the traveling salesman problem (TSP) by drawing a path that facilitates the process of transporting raw materials in the least time where the programming (Winqsb) was used. The most important thing that was reached is the mechanism of evaluating the suppliers by giving a percentage to each supplier and also their ranking where the supplier (Japanese company) got the first place and it was (87.3) ... etc. The raw materials process was used in the least time and obtained at the lowest time of the transfer, which is 292 minutes

Brand Digital Marketing under Intranet Security Control Based on the Machine Learning Classification Algorithm

With the advent of the information age, digital marketing models have begun to receive attention and to have applications in many industries. Although the digital marketing model has thus become a hot spot in the sales world, there is still not enough research on digital marketing. In order to optimize brand digital marketing under internal and external security control based on the machine learning classification algorithm, this paper uses fuzzy system theory to perform fuzzy analysis on various experimental data studied, convert it into a fuzzy set, obtain the fuzzy solution of the related function, establish related models of machine learning classification algorithms, and identify and collect relevant experimental data in an intelligent way, saving time for data collection. This paper collects the customer characteristics, customer sensitivity, brand promotion, and brand revenue of a brand within seven days; then uses the classification algorithm and collected data to predict and analyze the future data results; and uses the machine learning classification algorithm model formula to solve the correlation function. The final experimental results show that, in the digital marketing mode, network marketing brings 75% of the benefits to the brand, which is the highest among the four digital marketing models, and it has the best brand publicity level, 45%. At the same time, customers’ sensitivity to the brand reaches 50% under the network marketing model.

On Type-2 Fuzzy Sets and Type-2 Fuzzy Systems

One of the advantages of systems based on fuzzy logic (fuzzy systems) is the possibility of a soft switch from one set of values of input parameters of the system to another, when different conclusions are drawn for different sets of these values. A fuzzy set of type 2 is a direct generalization of an ordinary fuzzy set. In this paper, we review some branches of the theory of type-2 fuzzy sets and the theory of type-2 fuzzy systems. We discuss operations on type-2 fuzzy sets, type-2 fuzzy relations, and the centroids of type-2 fuzzy sets and describe type-2 functional fuzzy systems and type-2 relational fuzzy systems.