Linguistic Variables Research Articles

The Improved CURLI Method for Multi-Criteria Decision Making

Multi-Criteria Decision Making (MCDM) investigates the best available choice in the presence of multiple conflicting criteria, whereas the Collaborative Unbiased Rank List Integration (CURLI) method has been proposed recently and has been applied in various fields of daily life. However, most previous works concentrated on analyzing cases in which the factor of a criterion is a specific quantity. The present paper proposes an approach developed from the original CURLI method, named Improved CURLI. This improvement helps solve a problem when the factors of the criteria can be linguistic variables or a data set. The proposed method is applied to rank the alternatives for two case studies: choosing the best grinding wheel and the best service suppliers. The ranking results are compared to those obtained using other methods. Furthermore, sensitivity analysis is also conducted to examine the stability and reliability of the ranking results in various scenarios. The results demonstrate the validity of the Improved CURLI method and prove that it is applicable for making decisions in various fields.

Dynamic Analysis of Fuzzy Systems

In this work, a new methodology for the dynamic analysis of non-linear systems is developed by applying the Mamdani fuzzy model. With this model, parameters such as settling time, peak time and overshoot will be obtained. The dynamic analysis of non-linear fuzzy systems with triangular membership functions is performed, and linguistic variables describing overly complex or ill-defined phenomena are used to fit the model. Scaling factors will simplify the modification of the variables, making them easier to find the system model. The specifications of second-order characteristics in the time domain, such as overshoot and peak time, will be represented graphically. As a case study, the proposed methods are implemented to analyse the dynamics of a tank and a simple pendulum for first-order and second-order systems, respectively, where it is observed that the proposed methodology offers highly positive results.

Intuitionistic fuzzy DEMATEL for developing causal relationship of water security

PurposeThis study aims to develop a cause-effect relationship between criteria that contribute to water security using the Intuitionistic Fuzzy-Decision-Making Trial and Evaluation Laboratory (IF-DEMATEL) method. Differently from the typical DEMATEL which utilizes crisp numbers, this modification introduces intuitionistic fuzzy numbers (IFNs) to enhance judgments in a group decision-making environment. In particular, the linguistic variables used in IF-DEMATEL are defined using the concept of three-tuple of IFNs.Design/methodology/approachData with the linguistic variable “influence” were collected from a group of experts in water security via personal unstructured interviews. Seven water security criteria are considered in this study. Computational software was employed to execute the computational procedures of the IF-DEMATEL method. It is anticipated that by taking into account the hesitation degree of IFNs will reflect the scenario in real life, which could lead to precise decision-making.FindingsThe results show that “Over-ion”, “Saltwater Intrusion” and “Limited Infrastructures” are the cause criteria that contribute to water security. In addition, the relationship map of influence shows that “Water Pollution” and “Rapid Urbanization” are the most vulnerable criteria as these two criteria are most easily affected by other criteria in a unidirectional relation.Practical implicationsIt is anticipated that these findings will serve as useful references for water security management and policymakers.Originality/valueThe present study makes a noteworthy contribution to the modification of DEMATEL where three-tuple of intuitionistic fuzzy numbers are considered in the computations. The present study also provides additional evidence with respect to factors that contribute to water security.

A large-scale group consensus reaching approach considering self-confidence with two-tuple linguistic trust/distrust relationship and its application in life cycle sustainability assessment

Large-scale group decision making (LSGDM) is very common in real world, and especially how to reach a relatively consensus status in a social network is a hot topic. In this paper, we propose a concept of two-tuple linguistic trust/distrust relationship (LTR) which could present both trust and distrust degrees by semantics. The trust/distrust representation scheme can unburden individuals from providing numerical trust or distrust degree to just presenting linguistic variables. Transformation rule from two-tuple LTR to numerical trust degree is then analyzed, followed by the propagation operator of indirect trust/distrust relationships. The advantage of the propagation operator lies in that ignorance in trust/distrust relationships can be tackled rationally. As for the consensus reaching process (CRP), three-level identification and adjustment mechanisms are proposed under the condition that individuals express their preferences in an uncertain distribution form. Trust relationship, consensus degree and reliability of individuals’ judgments are all addressed comprehensively to narrow the opinion divergence. Self-confidence extent is utilized as a factor to adjust the opinions of non-consensus experts. The proposed method is further implemented in life cycle sustainability assessment to demonstrate the validity and effectiveness in dealing with realistic GDM problems.

The system for electric vehicle slippage prevention based on a fuzzy logic controller

Purpose. Development of a functional diagram of a slip prevention system, build a mathematical model, synthesize a fuzzy regulator, simulate and confirm the system's performance. Methodology. Mathematical analysis and modeling. Findings. The structure is substantiated and the linguistic variables of the fuzzy regulator are determined. The choice of the number of terms of the input and output variables of the fuzzy controller is made, proceeding from the minimization of the number of logical processing rules. The terms of the linguistic variables of the fuzzy controller are selected in the form of the simplest triangular for the mean values of the range of their definition and trapezoidal for the limit values. Logical processing rules were developed. The choice of the fuzzy inference algorithm was made and the defuzzification parameters were determined. A computer model of the slip prevention system was built using the Fuzzy logic toolbox of the MATLAB package and was framed using 9 logic processing rules. Computer simulation of the acceleration of an electric vehicle with subsequent slipping by one and two wheels has been carried out. The system maintains the inconsistency of wheel speeds with the electric vehicle speed at a constant set level of 1.5s-1 - in accordance with the formulated identification algorithm and completely prevents excessive slipping. Originality. The built-in phase regulator ensures minimization of the discrepancy between the speed of the wheel rotation and the linear speed of the electric vehicle, which in turn minimizes slippage and provides the maximum possible torque relative to the moment of installation. Practical value. Computer simulation was carried out for two modes: acceleration with a collision with a surface with a reduced coefficient of adhesion (0.1) with one wheel and acceleration with a collision with a surface with a reduced coefficient of adhesion (0.1) with two wheels. The system completely prevents excessive slipping.

Theoretical prediction of odour determining parameters in dairy effluent using adaptive neuro fuzzy inference system

<p>People are prompted to complain about air pollution by offensive odours. They can produce psychological consequences, such as nausea, headaches, lack of appetite, breathing difficulties, and various adverse reactions in some circumstances. Among various industries, the dairy industry emits the most noxious odours. The human nose is the only trustworthy sensor, and numerous laboratory and field methods for quantifying human observations have been devised. Most odours are difficult to quantify, as evidenced by the fact that contemporary technology has yet to provide a fully precise method for quantifying them. As a result, the current study examines the possibility of employing fuzzy logic to predict odour intensity from dairy effluent characterisation. In this work, an Adaptive Neuro Fuzzy Inference System (ANFIS) based prediction model using Sugeno controller is developed for finding the Odour Index of the dairy effluent. COD and BOD are considered as input parameters for the model with triangular membership function for five linguistic variables. The standard odour index estimated from the literatures is used for modelling. The predicted odour index obtained from the ANFIS model for synthetic dairy wastewater is validated against the standard odour index and the average error is found to be ± 0.16 %.</p>

Modeling the Covariance of Financial Assets Using Neutrosophic Fuzzy Numbers

This paper aims to model the covariance of financial assets using neutrosophic fuzzy numbers. Two main concepts are discussed and used, namely the neutrosophic covariance of the financial assets and the independent neutrosophic portfolios. In terms of methodology, a three-step approach is proposed with the purpose of identifying the independent neutrosophic portfolio return, the independent neutrosophic portfolio risk and the structure of the independent neutrosophic portfolio. For this purpose, neutrosophic fuzzy theory is chosen for this type of approach as it allows a proper modeling of the financial performance indicators by taking into account the probabilities of their achievement. This action is possible even in the situation in which linguistic variables are used for better characterizing the values of the recorded data. Numerical examples are provided in each stage of the methodology description for a better understanding of the proposed approach. The results of the study can be used to substantiate the decisions made by the capital market investors.

A consensus model for group decision-making with personalized individual self-confidence and trust semantics: A perspective on dynamic social network interactions

Natural language could be a common method for individuals to specific social relationships or self-evaluation (i.e., assured or unconfident). Hence, it is important to contemplate customized linguistics in collective decisions that involve linguistic variables, because the same linguistic term set may have totally different individual numerical scales. This research develops a consensus model taking into consideration the personalized individual self-confidence and trust semantics in dynamic social network group decision-making scenarios. Firstly, an optimization model driven by best additive consistency is proposed to derive the personalized self-confidence and trust semantics of individuals. Subsequently, a trust aggregation technique supporting the shortest path is outlined for weight distribution. The proposed technique not only considers the personalized individual trust linguistics but can also effectively decrease the risk of information distortion caused by trust decay. Moreover, a consensus feedback strategy is developed that combines opinion adjustment and dynamic trust interaction to enhance the potency of consensus. Totally different from most existing consensus models supported static trust network, the bestowed consensus feedback mechanism considers the inverse driving result of opinion adjustment on trust interaction within the consensus reaching processes. Finally, numerical examples and comparisons are provided to justify the practicability of the proposed model.

Combination of Hedge Algebra and Type-2 Fuzzy System for Electrocardiogram Signal Recognition and Classification

The paper presents a new application of Hedge Algebra in Type 2 (HAT2) Fuzzy System (FS) for electrocardiographical signal recognition and classification. The HAT2 integrates the capability of fuzzy logic in modeling uncertainties of data and the linguistic variables to describe and manipulate the knowledge in a way closer to the way humans describe facts and rules. An adaptive learning algorithm will be also proposed to train the system’s parameters for better fitting to the data samples. The proposed approach will be tested with the heart beat classification problem with different types of arrhythmias in the ECG signals taken from the Arrhythmia Databases of Massachusetts Institute of Technology and Boston's Beth Israel Hospital. The numerical results will be compared with other methods to show the high quality of proposed solution. The proposed solution is also shown being simple with low complexity of computation, which makes it suitable for use in IoT or portable devices for intelligent solutions used in modern healthcare systems.

Handwriting fluency, latency, and kinematic in Portuguese writing system: Pilot study with school children from 3rd to 5th grade.

Studies have referred to the interaction between orthographic and motor aspects during the production of handwriting. However, studies with Brazilian Portuguese are still lacking. Hence, the aim of this study was to compare orthographic regularity, based on the Portuguese writing system, in high (HF) and low (LF) frequency words, in relation to latency and kinematic variables in students from the 3rd to the 5th grade of elementary school. This is a cross-sectional pilot study, with a convenience sample of 95 children participated in this study, from 3rd to 5th grade level attending a state elementary school. All were submitted to the following procedures of computerized evaluation of handwriting and submitted to the task of writing 15 HF and 15 LF words, selected according to the frequency criteria and classified according writing coding rule. Results indicated that for HF words, there was a decrease in writing and disfluencies production time, for all coding rules, from 3rd to 5th grade. However, for LF words, the more unpredictable orthographic affect production duration time, movement fluency, and students became more dependent on the use of gaze to check spelling aspects. This study revealed that lexical and sub-lexical activation affected motor production. For HF and LF words, lexical and sublexical process favored motor programming. However, for LF words, despite the maturation and school progression for the motor planes, there was an increase in latency time and in the need to search for word information, measured by the gaze variable for words with greater irregularity. This study has provided some evidence that linguistic variables such as orthographic regularity and word familiarity affect handwriting performance in Brazilian Portuguese written language.

An Extended Interval Type-2 Fuzzy VIKOR Technique with Equitable Linguistic Scales and Z-Numbers for Solving Water Security Problems in Malaysia

Interval Type-2 Fuzzy VIseKriterijumska Optimizacija I Kompromisno Resenje (IT2FVIKOR) technique is one of the techniques of Interval Type-2 Fuzzy Multi-Criteria Decision Making (IT2FMCDM), which was developed to solve problems involving conflicting and multiple objectives. Most of the IT2FVIKOR methods are created from linguistic variables based on Interval Type-2 Fuzzy Set (IT2FS) and its generalization, such as Interval Type-2 Fuzzy Numbers (IT2FNs). Recent literature suggests that equitable linguistic scales can offer a better alternative, particularly when IT2FSs have some limitations in handling uncertainty and imbalance. This paper proposes the extended IT2FVIKOR with an equitable linguistic scale and Z-Numbers, where its linguistic scale introduces an equitable balance of positive and negative scales added to the restriction and reliability approach. Different from the typical IT2FVIKOR, which directly utilizes IT2FNs with a positive membership, the proposed method introduces positive and negative membership where each side considers a restriction and reliability approach. Besides, this paper also offers objective weights using fuzzy entropy-based IT2FS to calculate the weights of the extended IT2FVIKOR. The obtained solutions would help decision makers (DMs) identify the best solution to enhance water security projects in terms of finding the best strategies for water supply security in Malaysia.

Medical Fuzzy-Expert System for Assessment of the Degree of Anatomical Lesion of Coronary Arteries.

Today, cardiovascular diseases cause 47% of all deaths among the European population, which is 4 million cases every year. In Ukraine, CAD accounts for 65% of the mortality rate from circulatory system diseases of the able-bodied population and is the main cause of disability. The aim of this study is to develop a medical expert system based on fuzzy sets for assessing the degree of coronary artery lesions in patients with coronary artery disease. The method of using fuzzy sets for the implementation of an information expert system for solving the problems of medical diagnostics, in particular, when assessing the degree of anatomical lesion of the coronary arteries in patients with various forms of coronary artery disease, has been developed. The paper analyses the main areas of application of mathematical methods in medical diagnostics, and formulates the principles of diagnostics, based on fuzzy logic. The developed models and algorithms of medical diagnostics are based on the ideas and principles of artificial intelligence and knowledge engineering, the theory of experiment planning, the theory of fuzzy sets and linguistic variables. The expert system is tested on real data. Through research and comparison of the results of experts and the created medical expert system, the reliability of supporting the correct decision making of the medical expert system based on fuzzy sets for assessing the degree of anatomical lesion of the coronary arteries in patients with various forms of coronary artery disease with the assessment of experts was 95%, which shows the high efficiency of decision making. The practical value of the work lies in the possibility of using the automated expert system for the solution of the problems of medical diagnosis based on fuzzy logic for assessing the degree of anatomical lesion of the coronary arteries in patients with various forms of coronary artery disease. The proposed concept must be further validated for inter-rater consistency and reliability. Thus, it is promising to create expert medical systems based on fuzzy sets for assessing the degree of disease pathology.

The Integrated Novel Framework: Linguistic Variables in Pythagorean Neutrosophic Set with DEMATEL for Enhanced Decision Support

This study proposes a methodology that integrates the Pythagorean Neutrosophic Set (PNS) with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. This integration is intended to effectively handle the challenges of uncertainty and linguistic vagueness that are commonly encountered in multi-criteria decision-making scenarios. The PNS-DEMATEL framework comprises eight distinct steps, wherein the fundamental divergence lies in the creation of a linguistic variable within the PNS-DEMATEL framework. The linguistic variable has been formulated utilizing the PNS methodology, thereby enabling a more all-encompassing depiction of the viewpoints of experts. The application of the 7-point linguistic scale is utilized to acquire evaluations from experts, resulting in heightened precision and discernment. The method that has been put forth aims to improve the representation and management of linguistic variables, thereby enhancing the usability and validity of the model. The verification of the linguistic variable's validity is achieved by ensuring that the conditions outlined for the PNS are met. The integration of PNS with DEMATEL can facilitate the exploration of causal relationships in the barriers to halal certification. This approach can provide decision-makers with a more comprehensive and accurate representation of their opinions and judgements, leading to improved effectiveness in decision-making across various application domains.

Fuzzy linguistic forecasting of the results of the implementation of regional socio-economic projects

The article is devoted to the problem of improving the intellectual digital support for managing the implementation of regional socio-economic projects, which are an effective mechanism for the development of regions. Given the high level of uncertainty in the description of the subject area, it is proposed to use expert technologies to assess the degree of achievement of the intermediate and target indicators of the project. L. Zadeh's linguistic modeling apparatus was used as the basis for intellectual tools for representing and processing expert judgments. A linguistic indicative-temporal-territorial model of the project has been built in the form of a set of linguistic variables and a system of fuzzy production rules linking these variables. At the same time, temporal and territorial linguistic variables reflect the time and place of measurement of project indicators, the incomplete correspondence of which determines the degree of reliability of the measured values of indicators. The use of the constructed system of indicative-temporal-territorial fuzzy production rules makes it possible to link the intermediate values of the indicators with the target values of the project indicators and thereby predict the possible outcomes of its implementation. For this, a fuzzy logic inference procedure based on the use of the Mamdani algorithm has been developed. Preliminary results of using a research prototype of an intelligent expert system for analyzing the intermediate results of the project stages and predicting its results may indicate the effectiveness of the proposed approach.

Selection of goals with incomplete linguistic preference relations

In goal oriented requirements elicitation process (GOREP), the requirements of the stakeholders are refined and decomposed using AND/OR graph. Goal is a high level objective of a company which is refined and decomposed into sub-goals. After refinement and decomposition, a system may have several goals and it is not possible to implement all goals because of the time, budget, and other constraints of an organisation. It is a primarily research issue that how to select those goals that would be designed, implemented and tested during different phases of the software development process. On the basis of our review, we found that there is no support for the selection of goals in GOREP when preference relations are incomplete and the preferences of the decision-makers are represented by linguistic variables. Therefore, to address this research issue, we present a method for goal selection when linguistic preference relations are incomplete. Proposed method was applied to select the goals of the institute examination system.

Methods for optimizing data processing based on fuzzy adjustment of time series elements and identification model variables

The problem of optimal identification and processing of random time series (SVR) based on the properties of statistical, dynamic, and fuzzy models is formulated. A method for qualitative identification of SVR is proposed, which includes algorithms for fuzzy equations, logical inferences, taking into account the effects of environmental factors and non-stationary processes. A generalized algorithm for identifying SVR with adjustment and correction of variable values based on the rules of fuzzy logic, methods for searching for extrema by t -norms and s -norms is developed. Tools are designed for optimal data processing by determining an adequate model; parametric and structural identification of objects; search optimization; model training; identification of the “input and output” relationship; formation and use of a knowledge base, as well as sets of fuzzy rules, linguistic variables, membership functions, and algorithms for regulating variable values. Methods of fuzzy correction of distorted information by controlling the error of SVR identification are developed, and a software package is implemented that provides high accuracy of data processing with significantly lower costs.

A Fuzzy Model for Personnel Risk Analysis: Case of Russian-Finnish Export-Import Operations of Small and Medium Enterprises

Small and medium enterprises (SMEs) have limited resources for balancing risks which occur during international activities. The main hypothesis tested in this research is that the qualification of employees is the main area of personnel risks in cross–border cooperation. The fuzzy-logic model for personnel risks analysis was developed for quantification of the risks related to international activities. First, different risk factors and their elements were identified and formulated as linguistic variables. Second, with the use of experts’ judgments, a fuzzy logic-based system was constructed and evaluated. Risk level was calculated using MATLAB fuzzy logic toolbox and its factors were ranked accordingly. This model was applied to survey data from SMEs on Russia-Finland import-export operations during the 2020 – 2021 period. The personnel risk related to export-import Russia - Finland operations belonged to the above-average risk levels. Based on a more detailed analysis of risk elements, such elements as personnel development and training had the greatest coefficient and is an obvious high-risk area. The second highest value of the risk coefficient belonged to the element associated with personnel management. The lowest value belonged to elements related to motivation and recruitment processes. Therefore, theoretical contribution of the article is a model which allows us to quantify and identify micro-level personnel related risks in cross-border cooperation and present linguistic interpretation of these risks. This model can be use in practice by managers of specific SMEs or policy makers for obtaining broader and more representative results on risks related to international activities.

Experience in the use of artificial intelligence in making managerial decisions in the construction of real estate

The article deals with the problem of using artificial intelligence (AI) in the construction sector, since these systems are not being actively implemented, though AI capabilities can significantly simplify the entire process of erecting buildings and structures, including the development of improved business plans, adjusting the progress of the project directly on the construction site, making suggestions for management decisions in case of deviations from schedules and logistics plans. The experience of Chinese and domestic construction organizations in this area has been studied. A scheme for the implementation of an artificial intelligence system in construction production has been developed, the necessary resources for the development of machine learning in this area have been analyzed. It is concluded that for correct decision-making it is necessary to apply the principles of discrete mathematics, in particular, the distribution of expert assessments accepted as linguistic variables on the Harrington desirability scale with the construction of the membership function.

Quintile multiple regression with fuzzy coefficients and initial Z-information

The goal of the paper is to develop a regression model under the initial Z-information based on an alternative method to the least squares method, and free from the assumptions regarding probability distributions of initial data. Formalization of input and output information is carried out on the basis of Z-numbers and linguistic variables, followed by the construction of a multidimensional quintile regression model with fuzzy coefficients. The optimization function is defined as the sum of the loss functions for the differences between the weighted output fuzzy numbers and the weighted model fuzzy numbers. To determine the parameters of the unknown regression coefficients, a linear programming problem is solved to find the minimum of the optimization function. The developed Z-regression is free from the shortcomings of existing models and provides new opportunities for solving tasks in problem areas with the active participation of experts, taking into account the reliability of information received from them.

НЕЧЁТКО-МНОЖЕСТВЕННЫЙ АНАЛИЗ ОТРАСЛЕВОЙ ЭКОНОМИЧЕСКОЙ УСТОЙЧИВОСТИ

The aim of this publication is to review the approaches undertaken by our scientific school for modeling industry economic resilience in the paradigm of resilience. For the purpose of analyzing industry resilience, we employ fuzzy-logical modeling of a super-system (system of systems) consisting of impact subsystems, economic subsystem, and decision subsystems, packaged into a single model container of a 7x6 strategic matrix. All indicators in the 7x6 matrix in the forecasting model are represented as arbitrary fuzzy numbers or linguistic variables. As a result of our review, we present the following main phases of modeling: construction of industry resilience indices, modeling of external impacts, modeling of a balanced system of indicators (BSI), modeling of risks/opportunities, and modeling of decisions. The following technologies based on fuzzy descriptions are utilized in our modeling; matrix aggregate computing, BSI with fuzzy connections, R-lens, comparison of fuzzy sequences, and others. An example of unfolding the 7x6 matrix is presented using four key indicators from the BSI. The discussed modeling approaches have been applied to nine selected industrial and agricultural sectors, resulting in corresponding analytical assessments of resilience. The COVID-19 pandemic is considered as a demonstration example of impact. The demonstrated approaches to modeling resilience can be arbitrarily expanded and deepened, with the perspective of incorporating more sophisticated mathematical objects obtained at the intersection of fuzzy and probability paradigms into the 7x6 matrix model. Such inclusion can be driven by differentiating various types of uncertainty, where in one case it is appropriate to conduct modeling using non-statistical probabilities (e.g., in Bayesian schemes), in another case by utilizing possibility measures, and in a third case by structuring hybrid descriptions such as fuzzy probabilities of Puri-Ralescu type.