Showing papers on "Fuzzy mathematics published in 2021"

Integration performance statistics of green suppliers based on fuzzy mathematics and BP neural network

Abstract: With the enhancement of people’s environmental awareness, improving environmental performance has become an important way for manufacturing enterprises to achieve sustainable development. At present, one of the key challenges facing enterprises in terms of environmental sustainability is to extend it to other supply chain members. In this paper, the author analyzes the integration performance statistics of green suppliers based on fuzzy mathematics and BP neural network. Supply chain integration represents the company’s ability to formulate strategic alliances, integrate resources, establish seamless processes and share information. The empirical results show that it is feasible to evaluate the performance of green logistics enterprise integration. Through reasonable calculation method and model, the effective and reasonable evaluation results can be obtained. Enterprise managers can reasonably evaluate the key links of enterprise management, allocate enterprise resources correctly, completely and reasonably, minimize costs and maximize profits. Therefore, for the green logistics enterprises, should pay attention to the enterprise’s own performance evaluation, timely adjust their own development direction, plan and goal.

Granular fuzzy PID controller

Abstract: In this paper an uncertain dynamical system is investigated in which the coefficients are as a class of fuzzy sets and the fuzzy derivative is considered as the granular derivative. Furthermore, the notions of granular second order derivative of a fuzzy function, fuzzy overshoot, fuzzy rise-time, and fuzzy peak-time are introduced. As a result, designing a class of PID controllers called granular Fuzzy PID (gr-FPID) controller is presented based on fuzzy mathematics. The gr-FPID consists of granular integral, granular derivative with fuzzy coefficients. Moreover, the Particle Swarm Optimization (PSO) algorithm is used to tune gr-FPID fuzzy coefficients. It is demonstrated that the gr-FPID controller can effectively control the temperature in a continuous stirred tank reactor in which the parameters are uncertain.

Analysis of entrepreneurship education in colleges and based on improved decision tree algorithm and fuzzy mathematics

Abstract: In order to improve the performance of entrepreneurship and innovation education in colleges and universities, this study attempts to build an evaluation system and model of innovation and entrepreneurship in colleges and universities to provide a complete and practical tool for government education authorities and universities to evaluate the implementation of innovation and entrepreneurship education. In this research, decision tree and fuzzy mathematics are used as the basis of the model algorithm, and the algorithm is improved based on the analysis of traditional algorithms. Moreover, based on the improved decision tree algorithm, an evaluation index system for university innovation and entrepreneurship education is constructed. After determining the evaluation indicators of innovation and entrepreneurship education in colleges and universities, this study uses several universities as examples to analyze and define the definitions of various indicators. In addition, this study statistically analyzes the results of entrepreneurship and innovation education in colleges and universities through simulation. The research shows that the model proposed in this paper has a certain practical effect, and based on the simulation results, this study makes several suggestions.

Relations on neutrosophic soft set and their application in decision making

Abstract: Neutrosophic soft sets are a mathematical model put forward to overcome uncertainty with the contribution of a parameterization tool and neutrosophic logic by considering of information a falsity membership function, an indeterminacy membership function and a truth membership function. This set theory which is a very successful mathematical model, especially as it handles information in three different aspects, was first introduced to the literature by Maji (Ann Fuzzy Math Inf 5(1):157–168, 2013) and later modified by Deli and Broumi (J Intell Fuzzy Syst 28(5):2233–2241, 2015). In this way, they aimed to use neutrosophic soft sets more effectively for uncertainty problems encountered in most real life problems. Relations are a method preferred by researchers to explain the correspondences between objects. In this paper, neutrosophic soft relationships are discuss and define by referring to the theory of neutrosophic soft set proposed by Deli and Broumi (Ann Fuzzy Math Inf 9:169–182, 2015). Then, we present the concepts of composition, inverse of neutrosophic soft relations and functions along with some related properties and theorems. Moreover, the equivalence classes and equivalence relations of soft relations are given with support from real life examples and some of their properties are analyzed. Finally, we propose an algorithm to be used in expressing the correspondence between objects in solving uncertainty problems by using the soft relationship defined and an example is given to show how this algorithm can be applied for uncertainty problems.

Life Cycle Assessment of Bridges Using Bayesian Networks and Fuzzy Mathematics

Abstract: At present, reducing the impact of the construction industry on the environment is the key to achieving sustainable development. Countries all over the world are using software systems for bridge environmental impact assessment. However, due to the complexity and discreteness of environmental factors in the construction industry, they are difficult to update and determine quickly, and there is a phenomenon of data missing in the database. Most of the lost data are optimized by Monte Carlo simulation, which greatly reduces the reliability and accuracy of the research results. This paper uses Bayesian advanced fuzzy mathematics theory to solve this problem. In the research, a Bayesian fuzzy mathematics evaluation and a multi-level sensitivity priority discrimination model are established, and the weights and membership degrees of influencing factors were defined to achieve comprehensive coverage of influencing factors. With the support of theoretical modelling, software analysis and fuzzy mathematics theory are used to comprehensively evaluate all the influencing factors of the five influencing stages in the entire life cycle of the bridge structure. The results show that the material manufacturing, maintenance, and operation of the bridge still produce environmental pollution; the main source of the emissions exceeds 53% of the total emissions. The effective impact factor reaches 3.01. At the end of the article, a big data sensitivity model was established. Through big data innovation and optimization analysis, traffic pollution emissions were reduced by 330 tonnes. Modeling of the comprehensive research model; application; clearly confirms the effectiveness and practicality of the Bayesian network fuzzy number comprehensive evaluation model in dealing with uncertain factors in the evaluation of the sustainable development of the construction industry. The research results have made important contributions to the realization of the sustainable development goals of the construction industry.

An Application of Fuzzy Analytic Hierarchy Process in Risk Evaluation Model.

Abstract: Conflicts in land exploration are incisive social problems which have been the subject in many studies. Risk assessment of land conflicts is effective to resolve such problems. Specifically, fuzzy mathematics and the analytic hierarchy process were combined together to evaluate risk in land conflicts in our work, which is proved useful to solve uncertainty and imprecision problems. Based on the analysis of the principles for the risk assessment of a land conflicts index system, a set of risk assessment indexes using the fuzzy analytic hierarchy process (FAHP) was presented. The results show that the overall risk is at medium level, and the risk of the feasibility index and controllability index need to be paid more attention to. The contribution of this article is reflected in two aspects: (1) the application of FAHP in risk assessments of land conflicts is effective and valid; (2) it is helpful for governments to establish a stricter management system of work safety for conflicts in land exploration based on the risk assessment results.

Software test quality evaluation based on fuzzy mathematics

Abstract: The thesis starts with the connotation and attributes of software testing quality, introduces software testing quality evaluation methods, and analyzes and discusses software testing quality evaluation models based on fuzzy mathematics theory. Focusing on the key technical problems of software testing quality, discuss the key technologies to solve the software testing quality evaluation model establishment. Through the use of fuzzy models, the cost of software testing quality evaluation is effectively reduced, and the reliability of software testing quality evaluation methods is improved. This model can quickly evaluate the quality of software testing, can avoid the occurrence of local maxima, overcome the shortcomings of existing evaluation models and tools, and can correctly reflect the relationship between the internal and external properties of the software. Using the new software testing quality evaluation method, comparing the evaluation models and tools used before, summarizing the methods of software testing quality improvement. The application of these methods effectively improves the software testing quality.

Evaluation of English intercultural communication ability based on machine learning and fuzzy mathematics

Abstract: The process of international integration is accelerating continuously, which puts forward certain requirements for the current college students’ communicative ability and English ability. Therefore, it is necessary to further improve the students’ cross-cultural communicative ability in combination with English teaching. This paper combines machine learning and fuzzy mathematics methods to build an evaluation model of English cross-cultural communication ability. Moreover, based on the basic assumptions of college students’ oral communication ability evaluation, this paper builds a basic model for college students’ oral communication ability evaluation. In addition, through factor analysis and correlation analysis, this paper verifies the hypothesis of the student’s oral communication ability evaluation model and obtains an optimized university student’s oral communication ability evaluation model. After the model’s hypothesis testing and a series of statistical analysis, the evaluation system of college students’ oral communication ability is finally obtained. Finally, this article combines the investigation and analysis to test the performance of the model constructed in this article. The research results show that the capability evaluation model constructed in this paper has good performance.

Application of improved random forest algorithm and fuzzy mathematics in physical fitness of athletes

Abstract: The comprehensive indicators of the physical fitness of young athletes and the specific modes of transportation, working and leisure activities as explanatory variables are not in line with the normal distribution. Moreover, there is a high correlation between explanatory variables, and fitting traditional regression models does not meet the assumptions, and multiple collinearity problems will occur, and good results will not be obtained. The random forest regression model has excellent performance in overcoming these difficulties. Therefore, the random forest regression model is constructed to evaluate the impact of various factors on the physical fitness of young people. This paper studies the impact of various factors on the health level of young people’s body and combines the source data and research goals to establish a comprehensive evaluation index system and an influential factor indicator system. In addition, this paper uses AHP to conduct comprehensive evaluation, and obtains the comprehensive physical quality of young people, and gives corresponding suggestions according to the actual situation.

Similarity measure of the interval-valued fuzzy numbers and its application in risk analysis in paddy cultivation

Abstract: Generally, in a risk analysis problems, data are collected from experts in terms of linguistic variables, which are then observed as fuzzy numbers. Eventually, the total risk is calculated, and obtained as fuzzy numbers, using the arithmetic operations of fuzzy numbers. Finally, the risk so obtained has to be expressed in terms of linguistic variables, so that it becomes communicable to the general audience. One such tool in fuzzy mathematics is the similarity measure. In this paper, a new method of determining the similarity between two interval-valued fuzzy numbers has been developed. Generally, various parameters were used in deriving a similarity measure. However, the parameters that describe the shape of an interval-valued fuzzy numbers was never a concern in the earlier studies. Hence, in this study, these parameters are incorporated in the similarity measure. Further, a few reasonable properties are being developed, which will be considered as standard in developing a similarity measure. Finally, a risk analysis problem in paddy cultivation is discussed. Using the proposed similarity measure, the final risk is expressed in terms of a linguistic variable. The final risk so obtained for paddy cultivation, under the assumed scenario, is low.

Fuzzy mathematics model of the industrial design of human adaptive sports equipment

Abstract: Using the theoretical knowledge of ergonomics, physiology, and psychology, carried out the humanized design of human body adaptive sports equipment; using the theoretical knowledge of modeling, color, material science and other related disciplines, proposed the human body adaptive sports equipment Humanized design method. Aiming at the fuzziness of the industrial design of human adaptive sports equipment, fuzzy mathematics theory is used as an analysis tool, combined with the actual characteristics of the industrial design of human adaptive sports equipment, focusing on the nature of industrial design under fuzzy theory. Comprehensive analysis and understanding of design-related factors, and the introduction of expert survey method in the case of no basic data source in innovative design, to obtain the weight value of single factor influence, this will help designers grasp the main contradiction, achieve a targeted, and Solve key problems with higher efficiency. Subsequent use of fuzzy mathematics tools to put forward a quantitative index for the selection of options, which will help analyze the pros and cons of the various options, so as to provide a reliable reference for decision makers. This method makes up for the shortcomings of traditional methods in the process of design factor analysis and program selection.

Macro Modeling of V-Shaped Electro-Thermal MEMS Actuator with Human Error Factor

Abstract: The V-shaped electro-thermal MEMS actuator model, with the human error factor taken into account, is presented in this paper through the cascading ANSYS simulation model and the Fuzzy mathematics calculation model. The Fuzzy mathematics calculation model introduces the human error factor into the MEMS actuator model by using the BP neural network, which effectively reduces the error between ANSYS simulation results and experimental results to less than 1%. Meanwhile, the V-shaped electro-thermal MEMS actuator model, with the human error factor included, will become more accurate as the database of the V-shaped electro-thermal actuator model grows.

Design Trend Forecasting by Combining Conceptual Analysis and Semantic Projections: New Tools for Open Innovation

Abstract: In this paper, we describe a new trend analysis and forecasting method (Deflexor), which is intended to help inform decisions in almost any field of human social activity, including, for example, business, art and design. As a result of the combination of conceptual analysis, fuzzy mathematics and some new reinforcing learning methods, we propose an automatic procedure based on Big Data that provides an assessment of the evolution of design trends. The resulting tool can be used to study general trends in any field—depending on the data sets used—while allowing the evaluation of the future acceptance of a particular design product, becoming in this way, a new instrument for Open Innovation. The mathematical characterization of what is a semantic projection, together with the use of the theory of Lipschitz functions in metric spaces, provides a broad-spectrum predictive tool. Although the results depend on the data sets used, the periods of updating and the sources of general information, our model allows for the creation of specific tools for trend analysis in particular fields that are adaptable to different environments.

Comprehensive research on energy-saving green design scheme of crane structure based on computational intelligence

Abstract: In the field of cranes, unreasonable structure design leads to high energy consumption. In order to solve the problems of heavy weight and serious steel consumption of a crane structure, a green energy-saving design method based on computational intelligence is proposed. For minimizing the weight of a structure, two optimization models are proposed. The specular reflection algorithm is used to make the green and lightweight design. A multi-objective optimization model for the green design is constructed. The minimum waste generated in the manufacturing process is the objective function of this model. Fuzzy mathematics theory is utilized to comprehensively evaluate the impact of crane structure weight and processing waste on the environment, and a structural optimization model with fuzzy comprehensive evaluation indicators for the green design is introduced. The results indicate that compared with the original design, the processing waste after fuzzy comprehensive optimization is 63.43% lower and the cross-sectional area of the main girder is reduced by 27.03%.

Fault diagnosis of DC-DC module of V2G charging pile based on Fuzzy Neural Network

Abstract: Aiming at the problem of fault diagnosis of switching devices in DC/DC module of V2G charging pile, a diagnosis method based on fuzzy neural network is proposed. The method combines fuzzy mathematics with neural network, adopts 4-layer forward network and a step degree optimization algorithm, uses the self-learning and self-adaptive ability of neural network, adjusts the parameters of fuzzy set membership function in real-time, and trains a set suitable for V2G charging Fault diagnosis algorithm of DC/DC module of electric pile. Simulation results show that the fault diagnosis algorithm based on fuzzy neural network can effectively diagnose faults.

Big data visualization of the quantification of influencing factors and key monitoring indicators in the refined oil products market based on fuzzy mathematics

Abstract: In this paper, an in-depth study on the quantification of influencing factors and big data visualization of key monitoring indicators in the refined oil products market is carried out through fuzzy mathematical methods, and a system for quantifying influencing factors and big data visualization of key monitoring indicators in the refined oil products market with the fuzzy mathematical background is designed and implemented. The system realizes the functions of flow visualization, attack visualization, target tracking visualization, etc., and optimizes the system from the perspectives of performance and visualization effect. It achieves the display and interaction of multi-dimensional data in space and time with multiple views, angles, and dimensions. Data tagging and data correlation for key aspects of the product production process are realized through fuzzy mathematics and other means, and a quality traceability system for the manufacturing industry is realized on this basis, through which the data of some key stages of the product production process can be displayed retrospectively. The study proves that the business model of refined oil logistics platform based on value network can significantly improve the user’s perceived value and benefit all parties within the value network, realizing the complementary advantages of refined oil production enterprises and logistics platform companies, improving the efficiency of enterprise’s logistics and maximizing the profit of each subject within the value network to achieve profitability for all parties.

Safety inspection system and comprehensive evaluation method for concrete structure of gas pipeline tunnel based on fuzzy mathematics

Abstract: Regular safety inspections are an important guarantee to ensure the high-quality operation of concrete structures of tunnels. The Chinese West-east Gas Pipeline Project is large in scale and comple...

A fuzzy mathematical model for evaluation of rock-fracture and structural complexity: application for Southern Atlas in Tunisia

Abstract: In the present paper, a fuzzy model of assessment is proposed according the framework of fuzzy mathematical theory to estimate the complexity of rock fractures and geological structures. We established the fuzzy evaluation model and decided a trapezoidal membership function using the fuzzy mathematics. Dataset acquisition is integrated with an advanced technique of coupling multiscale fracture data obtained from both high-resolution satellite imagery and field data. Five parameters were selected, including qualitative and quantitative data from different scales, namely fractal dimension, fracture length, fracture intensity, the intersection angle between fractures and beddings and the fracture density. The weight of each factor is obtained with the principle that the relative value of the complexity index is larger and the weight is significant. This model was applied to the complexity assessment of the southwestern Tunisia. The rank of complexity of each sub-area in the study region is from strong to weak. The result shows that Jebal Orbata and Jebal Chamsi have the strongest complexity due to their position beside the 140 dextral strike-slip Gafsa fault. The east and central zone of the region of investigation has the weakest complexity.

Consumer Decision-Making Power Based on BP Neural Network and Fuzzy Mathematical Model

Abstract: In real life, because of the uncertainty of risk, incomplete information, perceived cost, and other factors, there are irrational behaviors in the decision-making power of consumers, so it is of great practical significance to study the decision-making power of consumers in the choice of countermeasures and personalized product recommendation. The purpose of this paper is to analyze the decision-making power of consumers based on the BP neural network and fuzzy mathematical model. First, the basic theory of artificial neural network and the concepts of set theory and fuzzy reasoning of fuzzy mathematics are described. Second, the behavior prediction model with the equal emphasis on rationality and irrationality and the integration of artificial neural network and fuzzy mathematics are constructed. The comments of a certain mobile phone are selected as the experimental objects to analyze the decision-making reasoning and prediction of individual consumers in the network and the decision-making reasoning of group consumers in the network, the experimental results show that through Mamdani reasoning, . Through the fuzzy set processing, it is finally determined that the consumer’s purchase intention is close to the VT mode, which is “very inclined.” In the first method, the user’s recognition rate of product C1 is 82%, and in the second method, the user’s recognition rate is 55%. The comparison of the two methods is in line with the expectation. The first method extracts the user’s emotion and evaluation information from the comments, fully considers the personalized needs of consumers, and is closer to the prediction results of the system.

Intelligent Decision-Making System for Martial Arts Competition Using Deep Learning

Abstract: In the field of martial arts, athletes can win the initiative in the competition if they can correctly and timely acquire the field knowledge, evaluate the situation efficiently, and formulate a suitable strategy. In this paper, we use fuzzy mathematics, mathematical statistics, and artificial intelligence learning algorithms to carry out systematic and in-depth research on the selection of Wushu competition scene decision-making. The fuzzy mathematics theory is combined with the intelligent design theory for decision-making based on a multiagent, case-based reasoning selection, and adaptability evaluation analysis. The Wushu competition scene decision system is constructed based on artificial intelligence learning algorithms. Our approach outperforms the existing approaches in terms of accuracy, sensitivity, specificity, and Matthew’s correlation coefficient (MCC). The results of our proposed model can be anticipated to have the potential for better flexibility and scalability in martial arts competition.

A New Ranking Method for Interval-Valued Intuitionistic Fuzzy Numbers and Its Application in Multi-Criteria Decision-Making

Abstract: Ranking of interval-valued intuitionistic fuzzy numbers (IVIFNs) is an important task for solving real-life Decision-Making problems. It is a potential area of research that has attracted the researchers working in fuzzy mathematics. Researchers worldwide are looking for a unique ranking principle that can be used to discriminate any two arbitrary IVIFNs. Various ranking functions on the set of IVIFNs have been proposed. However, every method has some drawbacks in ranking arbitrary IVIFNs due to the partial ordering. This paper introduces a new ranking principle for comparing two arbitrary IVIFNs by defining a new score function based on the non-membership value of IVIFNs. In this paper, firstly, the limitations of a few well-known and existing ranking methods for IVIFNs have been discussed. Secondly, a new non-membership score on the class of IVIFNs has been introduced. Thirdly, the superiority of the proposed score function in ranking arbitrary IVIFNs over the existing methods has been demonstrated. Finally, the proposed non-membership score function has been utilized in interval-valued intuitionistic fuzzy TOPSIS (IVIF-TOPSIS) using numerical examples.

Problem of Uncertainties in Geotechnical Engineering

Abstract: Different types of uncertainties are involved in the geotechnical engineering, for example, uncertainty of concept, uncertainty of material classification, soil parameters, constitutive or empirical models, boundary conditions, other uncertainty caused by measurement error, and so on. All these uncertainties affect the evaluation of the soil or structure performance, and they are required to be considered and analyzed for reliable evaluation results. According to the type of uncertainty, different analysis methods have been utilized to evaluate the uncertainty, for example, stochastic theory, fuzzy mathematics, theory of gray systems, artificial neural networks, genetic algorithms, and so on. In some cases, these methods are coupled to solve the uncertain problems. Among these methods, the probabilistic method in stochastic theory has been widely applied to many aspects of geotechnical engineering, for example, slope analysis, liquefaction analysis, reliability analysis of reinforced embankment, seepage analysis, soil–water characteristic curve, reducing uncertainties in empirical correlations, and so on.

Fuzzy Mathematics Comprehensive Forecasting Analysis of Metal Mine Rockburst Based on Multiple Criteriahere

Abstract: The underground environment in deep mines is complicated and the risk of rockburst is high during excavation. Therefore, it is of great significance to study the tendency of rockburst during excavation. At present, scholars from various countries have analyzed rockburst phenomena from the aspects of strength, stiffness, energy, etc., and have proposed various assumptions and criteria. However, these assumptions and criteria often only consider individual factors, which will lead to one-sidedness and limitations. This article takes the deep mining process of a metal mine as the research background. Based on the results of each independent criterion, fuzzy mathematical theory is introduced to evaluate the rockburst intensity and fuzzy comprehensiveness of various types of rocks in the project. The research results show that the multi-criteria comprehensive evaluation of fuzzy mathematics can well integrate the analysis results of each criterion, and has higher consistency with the actual situation in engineering.

Fuzzy soft information measures and their applications in dimension reduction and pattern recognition

Abstract: Soft set theory introduced by Molodtsov (Comput Math Appl 37:19–31, 1999) is an effective tool for solving realistic problems related to engineering, social sciences, medical sciences, and business. In the environment of vagueness and comprehension, Maji et al. (J Fuzzy Math 9:677–692, 2001) defined a new model known as a fuzzy soft set by hybridizing a fuzzy set with soft set. Recently, information measures for fuzzy soft sets have gained attention from researchers. In the present paper, two fuzzy soft information measures are proposed with the verification of their validity. Their applications in data dimension reduction and pattern recognition are also studied in detail and illustrated with numerical examples.

Use a novel figure of merit to evaluate the overall performance of SPAD

Abstract: Based on the typical performance indicators in single photon detectors, a novel figure of merit (FoM) is proposed to quantify the overall performance of SPADs (Single Photon Avalanche Diodes). The overall performance comparison exists not only between different devices but also between the same devices under different operating conditions. In this paper, the same device under different operating conditions is used as an example. To verify the validity of the novel figure of merit, a fuzzy mathematical model is introduced from the perspective of statistical mathematics. A compromise fuzzy decision-making method is used to analyze when a device achieves optimal performance under different operating conditions. The weights of the performance indicators required by the method are obtained by the combination weighting approach which combines the analytic hierarchy process and entropy weight method. The results show that the optimal operating conditions obtained by fuzzy mathematical analysis are consistent with the results obtained from the new FoM, and thus the novel FoM we proposed is feasible.

Improving the efficiency of technological units for the production of high-quality motor fuels using fuzzy mathematics methods

Abstract: This paper investigates the problems of increasing the efficiency of technological installations of oil refineries, which produce high-octane and environmentally friendly motor fuels. We used methods of mathematical modeling and fuzzy mathematics to maximize the volume of gasoline produced and improve its quality indicators, taking into account the imposed restrictions. On the basis of a systematic approach, an effective methodology is created for the development of a package of models of interconnected plant units using available information of a different nature, including fuzzy information. The resulting package of models makes it possible to systematically simulate the work of the unit under study and to increase the efficiency of the facility by increasing the volume of manufactured target products and improving its quality indicators. There are presented results of the analysis and expert assessment of the catalytic reforming unit LG-35-11/300-95 and the choice of the optimal type of model for individual units. A scheme is being created for combining the developed models into a single package of models. On the basis of the proposed methodology, hybrid models are being developed that make it possible to determine the volume of produced catalyzate and its quality indicators.