Fuzzy Mean Research Articles

Fuzzy Clustering Analysis of Power Incomplete Data based on Improved IVAEGAN Model

The scale of data generated by the complex and huge power system during operation is also very large. With the data acquisition of various information systems, it is easy to form the situation of incomplete power data information, which cannot guarantee the efficiency and quality of work, and reduce the security and reliability of the entire power grid. When incomplete data and incomplete data sets are caused by data storage failure or data acquisition errors, fuzzy clustering of data will face great difficulties. The fuzzy clustering of incomplete data of the power equipment is divided into the processing of incomplete data and the clustering analysis of "recovered" complete data. This paper proposes an IVAEGAN-IFCM interval fuzzy clustering algorithm, which uses interval data sets to fill in the incomplete data, and then completes the clustering of interval data. At the same time, the whole numerical data set is transformed into a complete interval data set. The final clustering result is obtained by interval fuzzy mean clustering analysis of the whole interval data set. Finally, the algorithm proposed in this paper and other machine learning training data sets is made for experimental analysis. The experimental results show that the algorithm proposed in this paper can complete incomplete data sets with high precision clustering. Compared with other contrast methods, it shows higher clustering accuracy. Compared with the numerical clustering algorithm, the clustering accuracy is improved by more than 4.3%, and it has better robustness. It also shows better generalization on the artificial data sets and other complex data sets. It is helpful to improve the technical level of the existing power grid and has important theoretical research value and engineering practice significance.

Retracted] Research on the Integration of Preschool Language Education Resources Based on Metadata Storage

Aiming at the problems of high redundancy and slow integration speed in the existing education resource data integration methods, a new preschool language education resource integration method based on metadata warehouse is designed. The metadata warehouse is designed, and the advantages of the integrated database are analyzed. On this basis, the sample data of preschool language education resources are classified with the help of cost matrix, and the constraints of different types of classification are set. The data collector of preschool language education resources is set up by using random forest algorithm to complete the data collection of preschool language education resources. The data of preschool language education resources are processed consistently, and the convergence of the data is calculated by edge function. On this basis, the redundant data in preschool language education data resources are characterized with the help of discourse, and the redundant data are removed to complete the data preprocessing of preschool language education resources. We determine the dimension distance between preschool language education resource data and complete the clustering integration of preschool language education resource data with the help of fuzzy mean clustering algorithm. The experimental results show that the integration method designed in this paper can reduce the redundancy in the integrated data, and the integration speed is fast.

A fuzzy mixed-integer robust design optimization model to obtain optimum settings of both qualitative and quantitative input variables under uncertainty

Quality improvement is the most effective activity for the process and product development cycle, while minimizing the process and product variation. For this particular purpose, robust design models are proposed to reduce the process and product variance. However, the majority of robust design models in the literature deals with certain situations. This article has four objectives. First, an experimental design matrix is generated for both qualitative and quantitative input variables under uncertainty. Secondly, triangular fuzzy numbers are used to measure the values of a response variable while dealing with an α-level cut strategy. Fitted fuzzy mean, standard deviation and variance response functions are also obtained. Thirdly, a fuzzy mixed-integer robust design optimization model is proposed to obtain the optimum operating conditions of input variables under uncertainty. Finally, a numerical example is presented to show the effectiveness of the proposed fuzzy-based methodology development for an uncertain environment.

Economic Management Data Envelopes Based on the Clustering of Incomplete Data

In this paper, the economic management data envelope is analyzed by an algorithm for clustering incomplete data, a local search method based on reference vectors is designed in the algorithm to improve the accuracy of the algorithm, and a final solution selection method based on integrated clustering is proposed to obtain the final clustering results from the last generation of the solution set. The proposed algorithm and various aspects of it are tested in comparison using benchmark datasets and other comparison algorithms. A time-series domain partitioning method based on fuzzy mean clustering and information granulation is proposed, and a time series prediction method is proposed based on the domain partitioning results. Firstly, the fuzzy mean clustering method is applied to initially divide the theoretical domain of the time series, and then, the optimization algorithm of the theoretical domain division based on information granulation is proposed. It combines the clustering algorithm and the information granulation method to divide the theoretical domain and improves the accuracy and interpretability of sample data division. This article builds an overview of data warehouse, data integration, and rule engine. It introduces the business data integration of the economic management information system data warehouse and the data warehouse model design, taking tax as an example. The fuzzy prediction method of time series is given for the results of the theoretical domain division after the granulation of time-series information, which transforms the precise time-series data into a time series composed of semantic values conforming to human cognitive forms. It describes the dynamic evolution process of time series by constructing the fuzzy logical relations to these semantic values to obtain their fuzzy change rules and make predictions, which improves the comprehensibility of prediction results. Finally, the prediction experiments are conducted on the weighted stock price index dataset, and the experimental results show that applying the proposed time-series information granulation method for time series prediction can improve the accuracy of the prediction results.

Models for MADM with hesitant interval-valued fuzzy information under uncertain environment

This paper is a research of interval-valued fuzzy and Muirhead Mean algorithms. We deduced new algorithms named as hesitant interval-valued fuzzy Muirhead Mean (HIVFMM) and hesitant interval-valued fuzzy Muirhead Mean (HIVFWMM) with Muirhead Mean algorithms based on Hesitant interval-valued fuzzy set (HIVFS). Firstly, we introduced some concepts and operation laws of HIVFS and the formula form of MM, then we combined them both and gave the proof process of properties and theorems, a mathematic model applying to MADM and a numerical example was given to illustrate the effectively and practically.

Regression Analysis of Factors Based on Cluster Analysis of Acute Radiation Pneumonia due to Radiation Therapy for Lung Cancer.

We conducted in this paper a regression analysis of factors associated with acute radiation pneumonia due to radiation therapy for lung cancer utilizing cluster analysis to explore the predictive effects of clinical and dosimetry factors on grade ≥2 radiation pneumonia due to radiation therapy for lung cancer and to further refine the effect of the ratio of the volume of the primary foci to the volume of the lung lobes in which they are located on radiation pneumonia, to refine the factors that are clinically effective in predicting the occurrence of grade ≥2 radiation pneumonia. This will provide a basis for better guiding lung cancer radiation therapy, reducing the occurrence of grade ≥2 radiation pneumonia, and improving the safety of radiotherapy. Based on the characteristics of the selected surveillance data, the experimental simulation of the factors of acute radiation pneumonia due to lung cancer radiation therapy was performed based on three signal detection methods using fuzzy mean clustering algorithm with drug names as the target and adverse drug reactions as the characteristics, and the drugs were classified into three categories. The method was then designed and used to determine the classification correctness evaluation function as the best signal detection method. The factor classification and risk feature identification of acute radiation pneumonia due to radiation therapy for lung cancer based on ADR were achieved by using cluster analysis and feature extraction techniques, which provided a referenceable method for establishing the factor classification mechanism of acute radiation pneumonia due to radiation therapy for lung cancer and a new idea for reuse of ADR surveillance report data resources.

Fuzzy stress‐strength reliability for weighted exponential distribution

AbstractThis study considers the reliability of the stress‐strength model in the presence of the fuzziness when the stress and strength variables have weighted exponential distribution with the common shape parameter. It obtains the mean remaining strength and fuzzy mean remaining strength for the weighted exponential distribution in order to calculate how long a component can live on average under the stress. The comparative simulation results with conventional and fuzzy approaches are presented to observe the effect of the parameter changes on the models using the maximum likelihood method for different sample sizes. In addition, the developed models are applied to two real data and estimation results were generated for two approaches.

Multidimensional analysis model of agricultural product supply chain competition based on mean fuzzy

Efficient and reliable fresh agricultural products supply chain is the key to meet the demand of consumers for fresh agricultural products, and also the guarantee for suppliers to realize their economic benefits. Therefore, a multi-dimensional analysis model of agricultural products supply chain competition based on fuzzy mean value is proposed. Firstly, the information distribution model of multi-dimensional analysis of agricultural product supply chain competition is proposed. On this basis, the multi-dimensional analysis information scheduling fusion of agricultural product supply chain competition is processed. Then, the application of mean value fuzzy in agricultural product supply chain is analyzed. According to the identification module of agricultural product information code, the fuzzy comprehensive evaluation model of supply chain and the mean fuzzy analytic hierarchy process, the competition of agricultural product supply chain is established Dimension analysis model. The experimental results show that the performance score of agricultural product supply chain is higher, the accuracy of supply chain information diagnosis is higher, and the clustering of agricultural product supply chain information diagnosis is better.

An automatic trading system for fuzzy portfolio optimization problem with sell orders

This paper deals with a portfolio optimization problem with sell orders, in which the investor will immediately sell the risky assets once their prices reach the preset sell thresholds. Departing from the traditional portfolio models, the investor needs to determine not only an investment proportion but also a sell threshold for each risky asset. We formulate the portfolio problem as a sequential decision problem and propose an automatic trading system with three stages to determine the investment strategy at the beginning of each period. In Stage 1, we formulate the return of an investment strategy as an LR-power fuzzy number based on the historical data and propose a fuzzy mean–semi-variance portfolio optimization model with sell orders. In Stage 2, we design a multi-objective genetic algorithm to solve the proposed model. In Stage 3, we select an optimal investment strategy among the efficient solutions based on the fuzzy Value-at-Risk ratio. Moreover, we conduct two case studies in the real stock market to illustrate the effectiveness and practicability of the proposed model and algorithm. The comparison results show that the proposed trading system has a better out-of-sample performance than the other ones.

Clinical Study of Virtual Reality Augmented Technology Combined with Contrast-Enhanced Ultrasound in the Assessment of Thyroid Cancer.

Thyroid cancer has become the most common malignant tumor in the endocrine system, and its global incidence has been showing an upward trend. The diagnosis methods of thyroid cancer include ultrasound, fine-needle aspiration cytology, and neck CT, but the single ultrasound feature cannot simultaneously take into account the sensitivity and specificity of more than 85% when diagnosing thyroid cancer. The development of virtual technology can significantly improve the diagnosis of the thyroid gland. Based on this, this article proposes a clinical study of virtual reality technology combined with contrast-enhanced ultrasound in the assessment of thyroid cancer. This article uses a variety of methods, such as literature method, mathematical statistics, and experimental research, in-depth study of the theoretical cornerstones of virtual reality augmented technology, the application status of ultrasound contrast technology, and so on. And a fuzzy mean clustering algorithm was proposed to identify ultrasound images. Then, a clinical experiment of virtual reality augmented technology combined with contrast-enhanced ultrasound was designed to evaluate thyroid cancer, which included comparison of contrast-enhanced ultrasound signs, analysis of enhancement results, multifactor logistic analysis, and diagnostic efficacy analysis of ultrasound signs. The combined application of virtual reality augmented technology and contrast-enhanced ultrasound in the study of thyroid cancer has a sensitivity and specificity exceeding 85% as the diagnosis boundary changes, and the accuracy of the combined diagnosis is relatively high.

Martingale convergence theorem for a conditional intuitionistic fuzzy mean value

The aim of this contribution is to show a representation of a conditional intuitionistic fuzzy mean value of intuitionistic fuzzy observables by a conditional mean value of random variables. We formulate a martingale convergence theorem for a conditional intuitionistic fuzzy mean value, too.

Interval-valued intuitionistic fuzzy multiple attribute decision making and their applications

In this paper, we expand the Muirhead mean (MM) operator and dual Muirhead mean (DMM) operator with interval-valued intuitionistic fuzzy numbers (IVIFNs) to propose the interval -valued intuitionistic fuzzy Muirhead mean (IVIFMM) operator, interval-valued intuitionistic fuzzy weighted Muirhead mean (IVIFWMM) operator, interval-valued intuitionistic fuzzy dual Muirhead mean (IVIFDMM) operator and interval-valued intuitionistic fuzzy weighted dual Muirhead mean (IVIFWDMM) operator. Then the MADM methods are proposed with these operators. In the end, we utilize an applicable example for green supplier selection in green supply chain management to prove the proposed methods.

New fuzzy mean codeword length and similarity measure

New fuzzy mean codeword length and similarity measure

Testing hypotheses for multivariate normal distribution with fuzzy random variables

There are several studies on fuzzy univariate hypothesis tests corresponding to a normal distribution. A fuzzy statistical test was proposed in this study for mean and variance–covariance matrix of a multivariate normal with fuzzy random variables. For this purpose, a notion of fuzzy multivariate normal random variable with fuzzy mean and non-fuzzy variance–covariance matrix was first developed. Then, the concepts of the fuzzy type-I error, fuzzy type-II error, fuzzy power, non-fuzzy significance level and fuzzy p-value were extended. A degree-based criterion was also suggested to compare the fuzzy p-values as well as a specific significance level to decide whether accepting or rejecting the underlying hypotheses. The effectiveness of the proposed fuzzy hypothesis test was also examined through some numerical examples.

Conditional Intuitionistic Fuzzy Mean Value

The conditional mean value has applications in regression analysis and in financial mathematics, because they are used in it. We can find papers from recent years that use the conditional mean value in fuzzy cases. As the intuitionstic fuzzy sets are an extension of fuzzy sets, we will try to define a conditional mean value for the intuitionistic fuzzy case. The conditional mean value in crisp intuitionistic fuzzy events was first studied by V. Valenčáková in 2009. She used Gödel connectives. Her approach can only be used for special cases of intuitionistic fuzzy events, therefore, we want to define a conditional mean value for all elements of a family of intuitionistic fuzzy events. In this paper, we define the conditional mean value for intuitionistic fuzzy events using Lukasiewicz connectives. We use a Kolmogorov approach and the notions from a classical probability theory for construction. B. Riečan formulated a conditional intuitionistic fuzzy probability for intuitionistic fuzzy events using an intuitionistic fuzzy state in 2012. In classical cases, there exists a connection between the conditional probability and the conditional mean value, therefore we show a connection between the conditional intuitionistic fuzzy probability induced by the intuitionistic fuzzy state and the conditional intuitionistic fuzzy mean value.

Generalized Fuzzy Soft Power Bonferroni Mean Operators and Their Application in Decision Making

In decision-making process, decision-makers may make different decisions because of their different experiences and knowledge. The abnormal preference value given by the biased decision-maker (the value that is too large or too small in the original data) may affect the decision result. To make the decision fair and objective, this paper combines the advantages of the power average (PA) operator and the Bonferroni mean (BM) operator to define the generalized fuzzy soft power Bonferroni mean (GFSPBM) operator and the generalized fuzzy soft weighted power Bonferroni mean (GFSWPBM) operator. The new operator not only considers the overall balance between data and information but also considers the possible interrelationships between attributes. The excellent properties and special cases of these ensemble operators are studied. On this basis, the idea of the bidirectional projection method based on the GFSWPBM operator is introduced, and a multi-attribute decision-making method, with a correlation between attributes, is proposed. The decision method proposed in this paper is applied to a software selection problem and compared to the existing methods to verify the effectiveness and feasibility of the proposed method.

Prediction of Student performance using Intuitionistic Fuzzy Mean Shift Clustering boosted with Chaotic Cheetah Chase Algorithm

It is an urgent desire for all the education institutions to predict the student’s academic performance at the earliest. Though, there are many factors are involved in discovering their performance, the most important factor focused in this paper is learning style of the individual student. In this work a novel method is constructed to cluster students learning style and with other facilities available to the students. The inconsistencies and impreciseness in identifying students by standard clustering models as Assimilator /Divergent / Converger / Accomadator is overcome by applying the intuitionistic fuzzy Mean shift clustering (IFMSC). This algorithm will intelligently tackle the outliers and the students whom lies in the border of the clusters by defining each student in terms of intuitionistic fuzzification with the assistance of mean shift method. The standard clustering models selects the centroids in an arbitrary manner which may have the possibility of failing to select the optimal instances as centroids during the initial stage itself. This problem is overcome in this proposed work by inheriting the Chaotic Cheetah Chase Algorithm(C3A) , which in turn is optimized by the chaotic theory while choosing the parameters and centroids. Thus, by applying the proposed IFMSC-C3A, the prediction of learning style of the students which influence their academic performance is accomplished more precisely while comparing the other clustering models.

Methods of coordinating fuzzy expert evaluations in the design of metrological support means

The article explores methods of coordinating expert judgments in the design of technical measuring instruments. A methodology for analyzing fuzzy matrices of pairwise comparisons of the effectiveness of preliminary designs of metrological support means is proposed. It includes defuzzification of pairwise comparison matrices; finding a median pairwise comparison matrix; determining fuzzy mean values for each element, and forming fuzzy weights for the compared means by individual levels of the membership function values. The procedure for finding the final membership function of metrological support tools is formalized to minimize the deviations of the fuzzy paired comparison matrices of expert opinions from the final matrix of these comparisons. Methods for optimizing the final ranking of alternative projects based on fuzzy group evaluations were investigated and developed. A fuzzy modification of the Condorcet principle is developed for the case of fuzzy dominance of alternatives. These methods of expert evaluations serve the purpose of increasing the efficiency of development and creation of metrological support means.

Multiple attribute group decision-making based on cubic linguistic Pythagorean fuzzy sets and power Hamy mean

The linguistic Pythagorean fuzzy sets (LPFSs), which employ linguistic terms to express membership and non-membership degrees, can effectively deal with decision makers’ complicated evaluation values in the process of multiple attribute group decision-making (MAGDM). To improve the ability of LPFSs in depicting fuzzy information, this paper generalized LPFSs to cubic LPFSs (CLPFSs) and studied CLPFSs-based MAGDM method. First, the definition, operational rules, comparison method and distance measure of CLPFSs are investigated. The CLPFSs fully adsorb the advantages of LPFSs and cubic fuzzy sets and hence they are suitable and flexible to depict attribute values in fuzzy and complicated decision-making environments. Second, based on the extension of power Hamy mean operator in CLPFSs, the cubic linguistic Pythagorean fuzzy power average operator, the cubic linguistic Pythagorean fuzzy power Hamy mean operator as well as their weighted forms were introduced. These aggregation operators can effectively and comprehensively aggregate attribute values in MAGDM problems. Besides, some important properties of these operators were studied. Finally, we presented a new MAGDM method based on CLPFSs and their aggregation operators. Illustrative examples and comparative analysis are provided to show the effectiveness and advantages of our proposed decision-making method.

Fuzzy Markovian Bonus-Malus Systems in Non-Life Insurance

Markov chains (MCs) are widely used to model a great deal of financial and actuarial problems. Likewise, they are also used in many other fields ranging from economics, management, agricultural sciences, engineering or informatics to medicine. This paper focuses on the use of MCs for the design of non-life bonus-malus systems (BMSs). It proposes quantifying the uncertainty of transition probabilities in BMSs by using fuzzy numbers (FNs). To do so, Fuzzy MCs (FMCs) as defined by Buckley and Eslami in 2002 are used, thus giving rise to the concept of Fuzzy BMSs (FBMSs). More concretely, we describe in detail the common BMS where the number of claims follows a Poisson distribution under the hypothesis that its characteristic parameter is not a real but a triangular FN (TFN). Moreover, we reflect on how to fit that parameter by using several fuzzy data analysis tools and discuss the goodness of triangular approximates to fuzzy transition probabilities, the fuzzy stationary state, and the fuzzy mean asymptotic premium. The use of FMCs in a BMS allows obtaining not only point estimates of all these variables, but also a structured set of their possible values whose reliability is given by means of a possibility measure. Although our analysis is circumscribed to non-life insurance, all of its findings can easily be extended to any of the abovementioned fields with slight modifications.