Fuzzy Phenomena Research Articles

Solution Strategy and Associated Results for Fuzzy Mellin Transformation

The advancement of the Mellin transformation is that it exhibits a scale-invariant nature, and thus, it is widely used in computer science. Fuzzy Mellin transformation can provide the purposes of such problems in uncertain phenomena. The existing studies on fuzzy Mellin transformation consider the fuzzy level cut approach, which makes the fuzzy valued functions into their crisp couplets regarding lower and uppercuts. This paper reconstructs a distinct mathematical frame for the Mellin transformation in a completely fuzzy environment in the sense of a modified Hukuhara derivative. In this paper, we mean the complete fuzzy environment to describe fuzzy phenomena where the conversion of the fuzzy function to its crisp counterpart is annulled. The proposed fuzzy Mellin transformation’s superiority over existing approaches is that it can be used directly to fuzzy valued functions without converting them to their crisp counter version. The challenges to dealing with fuzzy valued mappings and inputs without its level cut representation have been tackled in this manuscript. Furthermore, numerical examples, possible applications, and further extensions of this work are hinted at in this paper.

Three-way decisions in generalized intuitionistic fuzzy environments: survey and challenges

Enhancing decision-making under risks is crucial in various fields, and three-way decision (3WD) methods have been extensively utilized and proven to be effective in numerous scenarios. However, traditional methods may not be sufficient when addressing intricate decision-making scenarios characterized by uncertain and ambiguous information. In response to this challenge, the generalized intuitionistic fuzzy set (IFS) theory extends the conventional fuzzy set theory by introducing two pivotal concepts, i.e., membership degrees and non-membership degrees. These concepts offer a more comprehensive means of portraying the relationship between elements and fuzzy concepts, thereby boosting the ability to model complex problems. The generalized IFS theory brings about heightened flexibility and precision in problem-solving, allowing for a more thorough and accurate description of intricate phenomena. Consequently, the generalized IFS theory emerges as a more refined tool for articulating fuzzy phenomena. The paper offers a thorough review of the research advancements made in 3WD methods within the context of generalized intuitionistic fuzzy (IF) environments. First, the paper summarizes fundamental aspects of 3WD methods and the IFS theory. Second, the paper discusses the latest development trends, including the application of these methods in new fields and the development of new hybrid methods. Furthermore, the paper analyzes the strengths and weaknesses of research methods employed in recent years. While these methods have yielded impressive outcomes in decision-making, there are still some limitations and challenges that need to be addressed. Finally, the paper proposes key challenges and future research directions. Overall, the paper offers a comprehensive and insightful review of the latest research progress on 3WD methods in generalized IF environments, which can provide guidance for scholars and engineers in the intelligent decision-making field with situations characterized by various uncertainties.

A Pragmatic Analysis of Hedges in English Economic News: Taking the Financial Times as an Example

Since Professor L. A. Zadeh put forward the vagueness theory in 1965, a growing number of scholars have used it as a theoretical basis for preliminary discussions on the fuzzy phenomenon in many fields. However, there are not many studies on the analysis of the fuzzy phenomenon in news, especially the study of hedges in English economic news is yet to be enriched. Economic news plays an important role in people's daily lives. Economic news requires accuracy, objectivity, and conciseness, as well as acceptability and politeness. It seems to imply that hedges cannot exist in English economic news. However, the use of hedges in economic news can precisely achieve these purposes. To prove that there are a large number of hedges in economic news and that hedges play an important role in strengthening the credibility of news, avoiding giving details, achieving politeness, and enhancing self-protection, I take the news from the Financial Times as the source and explores the pragmatic function of hedges in English economic news and its specific application by combining the classification of hedges from the perspective of pragmatics, namely, strengthening the credibility, avoiding giving details, enhancing self-protecting and achieving politeness. By analyzing these results, the author hopes to contribute to the further development of economic news theory in English and also to help readers better grasp the essentials of economic news writing.

Simulation of Determining Outstanding Lecturers Using the Fuzzy Logic

In the learning process, lecturers have quite an important role because a lecturer is required to manage or organize so that the learning process can run well. The objectives of writing this article are Determine the category of outstanding lecturers by applying fuzzy logic with the mamdani method. Produce a mathlab program developed using the mamdani method to determine outstanding lecturers at Graha Nusantara University. Today, information technology, especially soft computing technology, is developing very quickly. One of the most developed software computing technologies is fuzzy logic, as it can be used to measure various ambiguous, camouflaged or fuzzy phenomena. One of the research topics applying fuzzy logic is the evaluation system in the research field. Research conducted by professors from Graha Nusantara Padangsidimpuan University in Simlitabmas data Still in the category of orientation to move to intermediate level, professors from UGN Padangsidimpuan are challenged to be able to develop, contribute and apply knowledge required for research. To do this, we needed an application that could be used to calculate and record teacher performance in outcomes research. The purpose of this study is to apply fuzzy logic with Mamdani method to evaluate the research efficiency of professors at Graha Nusantara Padangsidimpuan University this study uses Mamdani fuzzy logic.

Analysis of Improved Particle Swarm Algorithm in Wireless Sensor Network Localization

WSN localization occupies an important position in the practical application of WSN. To complete WSN localization efficiently and accurately, the article constructs the objective function based on the target node location constraints and maximum likelihood function. It avoids premature convergence through the PSO algorithm based on chaos search and backward learning. Based on linear fitting, the node-flipping fuzzy detection method is proposed to perform the judgment of node flipping fuzzy phenomenon. And the detection method is combined with the localization algorithm, and the final WSN localization algorithm is obtained after multi-threshold processing. After analysis, it is found that compared with other PSO algorithms, the MTLFPSO algorithm used in the paper has better performance with the highest accuracy of 83.1%. Different threshold values will affect the favorable and error detection rates of different WSNs. For type 1 WSNs, the positive detection rate of the 3-node network is the highest under the same threshold value, followed by the 4-node network; when the threshold value is 7.5 (3 ), the positive detection rate of the 3-node network is 97.8%. Different numbers of anchor nodes and communication radius will have specific effects on the number of definable nodes and relative localization error, in which the lowest relative localization error of the MTLFPSO algorithm is 3.4% under different numbers of anchor nodes; the lowest relative localization error of MTLFPSO algorithm is 2.5% under different communication radius. The article adopts the method to achieve accurate and efficient localization of WSNs.

A SUB-PIXEL VISUALIZATION METHOD TO DISPLAY FUZZY PHENOMENA USING RGB COLOR COMPOSITE (CASE STUDY: MANGROVES FOREST)

Natural phenomena boundaries and complexity of features in an urban area due to the low spatial resolution, lead to more pixels of satellite images included in reflectance of multiple land-cover/object components. The sub-pixel information extracting model outputs are fractional cover maps of interested class (end-member), with membership values between zero and one. These maps represented gradient change in only one fuzzy phenomenon boundaries such as vegetation cover. However, in multiple fuzzy class area or complex fuzzy phenomena such as mangrove forests, in the northwest of the Qeshm Island, Hormozgan, Iran, displaying several fractional covers may cause confusion and misunderstanding for the end-user. In this study, an additive color composite and spectral mixture analysis method is utilized for multiple fractional cover representation. The proposed method is implemented on images acquired from Operational Land Imager (OLI) sensor in the Landsat 8 satellite to extract three fractional covers (water, vegetation, and soil). An RGB color composite was used for each type and percentage of fractional cover for given pixel to display fractional cover separately. Based on such RGB color composite represented both quantitative and qualitative information, we used the RGB color solid cube as map legend for better understanding and map interpretation. The result of this study showed that suggested sub-pixel visualization method, gives new vision to the end-user understanding of fuzzy phenomena.

Hesitant Fuzzy Variable and Distribution

In recent decades, the hesitant fuzzy set theory has been used as a main tool to describe the hesitant fuzzy phenomenon, which usually exists in multiple attributes of decision making. However, in the general case concerning numerous decision-making problems, values of attributes are real numbers, and some decision makers are hesitant about these values. Consequently, the possibility of taking a number contains several possible values in the real number interval [0, 1]. As a result, the hesitant possibility of hesitant fuzzy events cannot be inferred from the given hesitant fuzzy set which only presents the hesitant membership degree with respect to an element belonging to this one. To address this problem, this paper explores the axiomatic system of the hesitant possibility measure from which the hesitant fuzzy theory is constructed. Firstly, a hesitant possibility measure from the pattern space to the power set of [0, 1] is defined, and some properties of this measure are discussed. Secondly, a hesitant fuzzy variable, which is a symmetric set-valued function on the hesitant possibility measure space, is proposed, and the distribution of this variable and one of its functions are studied. Finally, two examples are shown in order to explain the practical applications of the hesitant fuzzy variable in the hesitant fuzzy graph model and decision-making considering hesitant fuzzy attributes. The relevant research results of this paper provide an important mathematical tool for hesitant fuzzy information processing from another new angle different from the theory of hesitant fuzzy sets, and can be utilized to solve decision problems in light of the hesitant fuzzy value of multiple attributes.

Application of fuzzy analytic hierarchy process in risk assessment in medicine related fields

Risks exist in medicine related fields, which cannot be defined and quantified precisely. It is necessary to adopt a method for the risk assessment of uncertain and fuzzy phenomenon. This paper summarizes the thinking, procedure, advantage and application of fuzzy analytic hierarchy process in the risk assessment in medicine related fields for the purpose of providing reference for its further application.

The Special Semantics and Translation Strategies of Legal English From the Perspective of Cognitive Linguistics

As an interdisciplinary study, legal English translation involves three fields: law, linguistics and translation. Accordingly, legal English research should adopt an interdisciplinary perspective, whereas the current domestic research often overemphasizes the external legal literacy and neglects the internal structure of legal language. Therefore, translators sometimes misinterpret the special semantics of legal English. From the perspective of cognitive linguistics, legal English’s special semantics is mainly reflected in legal vocabulary and legal texts. Firstly, based on the metaphor theory of cognitive linguistics, the research will focus on the differences between the original domain and the target domain of legal English vocabulary, and analyze the metaphorical expression in legal language; secondly, based on the fuzzy phenomenon of cognitive linguistics, the research will interpret specific legal English texts to clarify the ambiguities; finally, the research will explore the legal English translation strategies through the cognitive interpretation of legal language.

The Scalar Mean Chance and Expected Value of Regular Bifuzzy Variables

As a natural extension of the fuzzy variable, a bifuzzy variable is defined as a mapping from a credibility space to the collection of fuzzy variables, which is an appropriate tool to model the two-fold fuzzy phenomena. In order to enrich its theoretical foundation, this paper explores some important measures for regular bifuzzy variables, the most commonly used type of bifuzzy variables. Firstly, we introduce the regular bifuzzy variables’ mean chance measure and some properties, including self-duality and its calculation formulas. Furthermore, we also investigate the mean chance distribution for strictly monotone functions of regular bifuzzy variables based on the proposed operational law. Finally, we present the expected value operator as well as equivalent analytical formulas of the expected value of regular bifuzzy variables and their strictly monotone functions.

Application of Quality Function Development Method to Establish Application of New Product Development Information System

In the process of new product development, the customer's feeling is usually fuzzy phenomenon, how to evaluate various factors is to test the developer's intelligence, this study takes the new product development process as the research object, and applies the Quality Function expansion (QFD) method to establish a decision support system with fuzzy processing ability. In this study, the first development of quality function expansion (QFD) applied to Customer voice collection and analysis and conversion to product specifications. Then, the integration of fuzzy theory and the provision of different commodity development solutions as the best choice for products.

Representation and Aggregation of Multi-source Information of Modern Smart Cities Based on the Intuitionistic Polygonal Fuzzy Set

The aggregation of multi-source information can be realized by means of the ordered representation of polygonal fuzzy sets and their linear operations, especially the ordered representation plays an important role in multi-attribute decision analysis. An intuitionistic fuzzy set is not only a generalization of traditional fuzzy set, but also can overcome some defects of fuzzy phenomena described by binary logic. In this paper, we first propose the ordered representation and its arithmetic operations of n-intuitionistic polygonal fuzzy sets (n-IPFS) based on the idea of equidistant dissection, and give a method to solve the ordered representation of n-IPFS through an example. Secondly, a mathematical linear function describing multi-source information of modern smart city is given through an ordered representation of n-IPFS, a new normalization method and calculation formula are proposed for the intuitionistic polygonal decision matrix. Then, by using n-IPFS arithmetic operations the aggregation method of weighted average operator and geometric average operator of multi-source information is given. Finally, the effectiveness of the proposed method is verified by aggregating multi-source information, and thus an intuitionistic fuzzy set and polygonal fuzzy set are unified and applied to decision-making problem of multi-source information of modern smart cities.

Dual possibilistic regression models of support vector machines and application in power load forecasting

Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.

Dual possibilistic regression analysis using support vector networks

This study proposes a novel and efficient dual regression model for possibilistic regression analysis that incorporates the principles of support vector machine (SVM) theory. The dual regression model, which comprises an upper model and a lower model, approximates the observed fuzzy phenomena from the outside and inside directions, respectively, such that the inclusion relationship between those two models holds. The proposed dual regression model better explains the inherent vagueness that exists in a given dataset. It provides the outer and inner bounds for the estimated vagueness region, and allows an estimation of the degree of confidence in the predicted fuzzy output. Using the principles for a twin support vector machine (TSVM), the upper- and lower models are estimated by solving two smaller SVM-type quadratic programming problems (QPPs), instead of a single larger QPP. This strategy significantly increases the learning speed for the proposed algorithm. The structural risk minimization principle of SVM makes the proposed method to yield better generalization ability. The kernel function method offers a model-independent framework for the proposed dual regression model. This paper focuses on the class of radial kernels, which enables the proposed method to conquer the problem of increasing spreads. The radial kernel also gives the proposed method a unified framework that allows both crisp and fuzzy inputs. The experimental results verify the effectiveness and efficiency of the proposed method. In comparison with previous SVM-based dual regression model, the proposed approach significantly improves the sparsity, prediction speed, and training speed.

A New Default Probability Calculation Formula and Its Application under Uncertain Environments

In the real world, corporate defaults will be affected by both external market shocks and counterparty risks. With this in mind, we propose a new default intensity model with counterparty risks based on both external shocks and the internal contagion effect. The effects of the external shocks and internal contagion on a company cannot, however, be observed, as uncertainty in the real world contains both randomness and fuzziness. This prevents us from determining the size of the shocks accurately. In this study, fuzzy set theory is utilized to study a looping default credit default swap (CDS) pricing model under uncertain environments. Following this, we develop a new fuzzy form pricing formula for CDS, the simulation analysis of which shows that all kinds of fuzziness in the market have a significant impact on credit spreads, and that the credit spreads, relative to the degree of external shock fuzziness, are much more sensitive. Nevertheless, for a certain degree of fuzziness in the market, credit spreads, relative to changes in counterparty risk, are much more sensitive. Using random analysis and fuzzy numbers, one can think of even more uncertain sources at play than the processes of looping default and investor subjective judgment on the financial markets, and this broadens the scope of possible credit spreads. Compared to the existing related literature, our new fuzzy form CDS pricing model with counterparty risk can consider more factors that influence default and is closer to the reality of the complexity of the dynamics of default. It can also employ the membership function to describe the fuzzy phenomenon, enable the fuzzy phenomenon to be estimated in two kinds of state, and can simultaneously reflect both the fuzziness and randomness in financial markets.

The Importance of the Ideological and Political Education Research Theory of Teaching Management in Colleges and Universities

Ideological and political education is the core link of the teaching management in colleges and universities. In educational activities, ideological and political quality of college students should be put in the first place. Under the new situation of social transformation, all kinds of social problems appear at the same time, has brought unprecedented clash of civilizations and cultures. History and reality, tradition and modernity, the local culture and the multiple factors of Western civilization, it may bring about the confusion and fuzzy phenomenon of political belief, value orientation and so on. With the development of the socialist market economy and the deepening under the background of the reform and opening-up, with a variety of ideological and cultural environment of mutual agitation, college students are influenced by all kinds of ideology and culture, and the ideological and political education of college students are facing severe challenges. For this kind of situation, analyze the solution of the challenges and explore the ideological and political education, further strengthening and improving the ideological and political education of university students in the new era is a major and urgent strategic task, which has great and far-reaching strategic significance.

Information Fusion Based on Information Entropy in Fuzzy Multi-source Incomplete Information System

With the development of society, although the way that people get information more and more convenient, the information which people get may be incomplete and has a little degree of uncertainty and fuzziness. In real life, the incomplete fuzzy phenomenon of information source exists widely. It is extremely meaningful to fuse multiple fuzzy incomplete information sources effectively. In this study, a new method is presented for information fusion based on information entropy in fuzzy incomplete information system and the effectiveness of the new method is verified by comparing the average fusion method. Then, an illustrative example is delivered to illustrate the effectiveness of the proposed fusion method. Finally, we have also tested the veracity and validity of this method by experiment on a dataset from UCI. The results of this study will be useful for pooling the uncertain data from different information sources and significant for establishing a distinct direction of the fusion method.

A new measure of divergence with its application to multi-criteria decision making under fuzzy environment

Divergence measure is an important tool for determining the amount of discrimination between two probability distributions. Since the introduction of fuzzy sets, divergence measures between two fuzzy sets have gained attention for their applications in various fields. Exponential entropy measure has some advantages over Shannon's entropy. In this paper, we used the idea of Jensen---Shannon divergence to define a new divergence measure called `fuzzy Jensen-exponential divergence (FJSD)' for measuring the discrimination/difference between two fuzzy sets. The measure is demonstrated to satisfy some very elegant properties, which shows its strength for applications in multi-criteria decision-making problems. Further, we develop a method to solve multi-criteria decision-making problems under fuzzy phenomenon by utilizing the proposed measure and demonstrate by a numerical example.

Fuzzy Neural Networks in the Assessment of Environmental Safety

The article focuses on the problem of assessing the level of the fuzzy phenomena as environmental safety territorial entities. The study presents a solution to this problem by neural networks. Application of neural networks allows to overcome the lack of available information on the input and to carry out the correct assessment of the environmental safety of territorial entities. Method of training a neural network is used for this. The advantages of using fuzzy neural networks are the flexibility and the opportunity to work directly with implicit parameters and criteria known to approximate or unknown data; a tolerance of ambiguities and contradictions of used data. This technique allowed solve the following tasks: to carry out a typology of subjects of the Russia in terms of environmental safety and to develop scenarios increase their security; to perform a forecast of changes in their development, taking into account environmental factors.

On analytic functions of complex Liu process

A complex stochastic process is a fundamental connection between two-dimensional stochastic processes. Similarly, a complex fuzzy process will connect two fuzzy processes to describe the spacial dynamic fuzzy phenomena. In this paper, we first give the formal definitions of complex fuzzy process and complex Liu process. And then the complex differential and complex Liu integral of complex Liu process are also studied in this paper. Especially, some properties of analytic functions of complex Liu process are proved. Finally, complex fuzzy differential equations are proposed as the extension of general fuzzy differential equation. The obtained results provide fundamentals and promote the development of fuzzy calculus.