Fuzzy Decision Theory Research Articles

A novel Hellinger distance-based regret theory method for spherical fuzzy decision making model and its application in logistics

Actual decision making problems are often based on the company decision maker’s behavior factors, such as risk attitude, subjective preference, etc. Regret theory can well express the behavior of the decision maker. In this pursuit, a novel decision making method was developed, based on the regret theory for the multi-attribute decision making problem, in which attribute values were expressed by spherical fuzzy numbers. Distance measurement not only has extensive applications in fields such as pattern recognition and image processing, but also plays an important role in the research of fuzzy decision theory. The existing distance measures of spherical fuzzy set either have special cases of anti-intuition or are more complex in calculation, so finding suitable distance measures is also an important research topic in the decision-making theory of spherical fuzzy set. For this reason, we first establish a new distance of spherical fuzzy sets based on Hellinger distance of probability distribution. A decision maker’s perception utility value function is proposed using the new distance formula, which is used to measure the regretful and rejoice value. Then we establish an optimization model for solving the attribute weights, when the information of attribute weight was partially known. Subsequently, the comprehensive perceived utility values were utilized to rank the order of the alternatives. Finally, a numerical example of assessment of logistics providers is used to show that the new decision making method is effective and feasible.

Hybrid seismic vulnerability models for regional structures considering bivariate intensity measures

The multiple-probability decision-making method for earthquake hazards considers multiple performance indicators and can be used to effectively estimate the seismic risk of structures. The seismic vulnerability model directly affects the development of the earthquake risk distribution in regional structures. This paper innovatively developed a bivariate intensity measurement method based on macroscopic and instrumental seismic intensities. An optimized instrument intensity calculation model was established considering the actual monitoring data of historical earthquakes in China. A hybrid seismic vulnerability quantification method based on empirical, expert judgement and analysis has been proposed and applied to evaluate the vulnerability levels of three types of building clusters in two typical earthquakes in Qinghai Province, China (the 2008 Dachaidan (DCD) earthquake and the 2009 Haixi (HX) earthquake). A structural hybrid vulnerability matrix and surface for nine zones based on bivariate intensity measures were developed. A synthetic vulnerability membership index considering fuzzy failure criteria and decision theory was proposed, and vulnerability membership curves for different vulnerability levels were established. An improved earthquake economic loss prediction function is proposed, and synthetic economic vulnerability index prediction models for three typical buildings in different regions are established. The estimated and calculated results of the developed multiscale variable parameter synthetic vulnerability model are highly consistent with the actual earthquake loss observations.

A unified theory of acceptance and use of technology and fuzzy artificial intelligence model for electric vehicle demand analysis

This study aims to reveal consumers’ intention to purchase Electric Vehicles (EVs) based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A hybrid fuzzy decision-making model with three stages is proposed. First, the experts’ weights are computed using an artificial intelligence methodology. Second, eight UTAUT-based indicators are examined using a T-Spherical TOPSIS-based DEMATEL (TOP-DEMATEL) methodology. The criteria are weighted by using multi-SWARA (M-SWARA) methodology. Third, an evaluation is conducted for the seven emerging countries by considering a Spherical Fuzzy (SF) Additive Ratio Assessment (ARAS) technique. The main contribution of this study is that a new decision-making methodology can identify more significant determinants of intention to use EVs. The methodological contribution of this study is integrating artificial intelligence methodology with fuzzy decision-making theory. The findings demonstrate that environmental factors play the most significant role in the intention to use EVs. Additionally, performance expectancy is also another critical determinant. We also find environmental issues should also be given importance in the production process of EVs. Using fossil fuels while producing these vehicles will significantly reduce users’ confidence. This phenomenon will cause consumers with environmental awareness not to purchase these vehicles.

Weighted continuous triangular fuzzy conflict analysis and its three-way decision method1

In the existing conflict analysis models, they used a triangular fuzzy number on [0, 1] to describe the range of an agent’s attitude towards an issue, but there are still some shortcomings in describing the specific attitude and degree of conflict represented by the triangular fuzzy number. In this paper, the conflict analysis model is extended, improved and perfected. Firstly, the expectation of triangular fuzzy number is used in the [-1, 1] triangular fuzzy information system to reasonably express the specific attitudes represented by a triangular fuzzy number. Secondly, the weights of each issue are obtained by using the Sugeno measure, which determines the total attitude of the agent towards all issues. Thirdly, the relationship between agents is obtained with the help of the weighted distance of triangular fuzzy numbers. Finally, the thresholds α and β are calculated by means of triangular fuzzy decision theory rough sets.

Integration of mobile power-hydrogen storage systems in distribution-level networks: A fuzzy information gap optimization framework

In recent years, renewable energy sources (RESs) have had unquestionably significant benefits. However, maximum penetration of RESs has always been challenging due to their intermittent nature. To address this challenge, this study advances a framework for the flexibility of the distribution network (DN) by employing transportable electrical storages (TESs) and transportable hydrogen storages (THSs). To do so, the TESs are considered to transfer electrical energy among high-penetrated and high-demand points. In addition, curtailed renewable power can be converted to green hydrogen and transferred to hydrogen refueling stations (HRSs) using the THSs. To further flexibility, this work considers an incentive-based demand response (DR) program and vehicle-to-parking lot (V2PL) capable plug-in electric vehicles (PEVs). Moreover, a binary-based method is developed to carry out the trajectory planning of the TESs and THSs and the Floyd algorithm is exploited to minimize traveling and simulation time. Furthermore, the fuzzy information gap decision theory (IGDT) is proposed to enhance the scheduling robustness against the uncertainty of RESs. The obtained results corroborated the positive impact of the TESs and THSs by declining the operation cost by about 4.8 %. Moreover, the proposed uncertainty management model is quite robust against RESs’ uncertainty by achieving about 37.5 % less operation cost.

Analysis and Applications of Artificial Intelligence in Digital Education Based on Complex Fuzzy Clustering Algorithms

Digital education is very important and valuable because it is a subpart of artificial intelligence, which is used in many real-life problems. Digital education is the modern utilization of digital techniques and tools during online purchasing, teaching, research, and learning and is often referred to as technology-enhanced learning or e-learning programs. Further, similarity measures (SM) and complex fuzzy (CF) logic are two different ideas that play a very valuable and dominant role in the environment of fuzzy decision theory. In this manuscript, we concentrate on utilizing different types of dice SM (D-SM) and generalized dice SM (GD-SM) in the environment of a CF set (CFS), called CF dice SM (CFD-SM), CF weighted dice SM (CFWD-SM), CF generalized dice SM (CFGD-SM), and CF weighted generalized dice SM (CFWGD-SM), and also derived associated outcomes. Furthermore, to evaluate or state the supremacy and effectiveness of the derived measures, we aim to evaluate the application of artificial intelligence in digital education under the consideration of derived measures for CF information and try to verify them with the help of several examples. Finally, with the help of examples, we illustrate the comparison between the presented and existing measures to show the supremacy and feasibility of the derived measures.

Development of feature extraction method based on interval-valued Pythagorean fuzzy decision theory

Feature extraction is an important step in image processing and the accuracy of the extraction affects the result of subsequent target detection. There are many feature extraction algorithms, but there are few theories and methods about the accuracy of the extraction. Moreover, most of the existing extraction methods are computationally complex, and they will expose deficiencies once they encounter a large number of standard cases. In order to fill this gap, this study proposes a new soft computing multi-criteria group decision-making (MCGDM) method based on interval-valued Pythagorean fuzzy set information to deal with situations where multiple criteria are involved in complex decisions. Thus, the power average operator is used to eliminate the effects of unreasonable input arguments on decision-making results, and the Maclaurin symmetric mean operator is used to comprehensively consider the mutual relationship between input arguments in this article. Combining with the concept of IVPFS, the interval-valued Pythagorean fuzzy power Maclaurin symmetric mean (IVPFPMSM) operator and weighted IVPFPMSM operator are proposed. Then, the TOPSIS method is used to determine the weight vector of decision-making set, and a MCGDM method is developed under the interval-valued Pythagorean fuzzy framework. Next, aiming at the defects of the existing evaluation criteria, we consider the different ranges of membership degree and non-membership degree to propose a new evaluation criterion of MCGDM method. Finally, the proposed method is applied to the optimal selection of characteristic interval of foreign fibers to verify its practicability and effectiveness. The proposed method has certain guiding significance for the extension of decision-making fuzzy operators and the exploration of feature extraction methods.

A fuzzy neural network-based automatic fault diagnosis method for permanent magnet synchronous generators.

In recent years, automatic fault diagnosis for various machines has been a hot topic in the industry. This paper focuses on permanent magnet synchronous generators and combines fuzzy decision theory with deep learning for this purpose. Thus, a fuzzy neural network-based automatic fault diagnosis method for permanent magnet synchronous generators is proposed in this paper. The particle swarm algorithm optimizes the smoothing factor of the network for the effect of probabilistic neural network classification, as affected by the complexity of the structure and parameters. And on this basis, the fuzzy C means algorithm is used to obtain the clustering centers of the fault data, and the network model is reconstructed by selecting the samples closest to the clustering centers as the neurons in the probabilistic neural network. The mathematical analysis and derivation of the T-S (Tkagi-Sugneo) fuzzy neural network-based diagnosis strategy are carried out; the T-S fuzzy neural network-based generator fault diagnosis system is designed. The model is implemented on the MATLAB/Simulink platform for simulation and verification, the experiments show that the T-S fuzzy diagnosis strategy is significantly improved, and the design purpose is achieved. The fuzzy neural network has a parallel structure and can perform parallel data processing. This parallel mechanism can solve the problem of large-scale real-time computation in control systems, and the redundancy in parallel computation can make the control system highly fault-tolerant and robust. The fault diagnosis model based on an improved probabilistic neural network is applied to the fault data to verify the effectiveness and accuracy of the model.

Bi-level sizing optimization of a distributed solar hybrid CCHP system considering economic, energy, and environmental objectives

In this paper, a distributed solar hybrid combined cooling, heating, and power (CCHP) system is constructed by installing photovoltaic/thermal (PV/T) panels in the CCHP (PV/T-CCHP) system. A bi-level optimization model is established to obtain the optimal sizing of the equipment in the PV/T-CCHP. The upper-level optimization model is formulated as a multi-objective optimization problem, which considers the economic, energy, and environmental performance of the PV/T-CCHP system compared with the conventional separated production system. The equipment sizing results obtained by the upper-level optimization model can achieve the trading off in terms of cost, primary energy consumption, and carbon emission reduction. The lower-level optimization model, which is formulated as a mixed-integer linear programming, determines the operating strategy of the PV/T-CCHP system. Non-dominated sorting genetic algorithm-II is employed to solve the upper-level optimization model. The final optimal sizing results are obtained from the Pareto solution set leveraging fuzzy decision theory. The optimal sizing results demonstrate that PV/T-CCHP saves 5.82% in annual total cost, 22.63% in annual primary energy, and 37.51% in annual carbon emissions reduction, which verifies the effectiveness of the proposed optimization model. In addition, the impacts of solar energy and energy storage equipment on the system performance are quantitatively evaluated.

Decision-Making Research on Tour Lines of Tourist-Dedicated Train Based on Vague Sets and Prospect Theory

Tourist-dedicated train is the product of the combination of railway transportation and tourism, and its tour line selection includes the selection of nodes and lines. Based on the principle of decision-making, taking technical factors, tourism factors, regional economic factors, and passenger flow factors as criteria, this paper analyzes the decision-making indices affecting the tour line and establishes the decision-making index system. Subsequently, the prospect theory considering the bounded rationality and psychological factors of decision makers is combined with vague set fuzzy decision theory, and a comprehensive decision method based on vague set and prospect theory is proposed. Finally, the feasibility of the proposed decision method is verified by an example. The research shows that the established decision-making index system is representative, and the proposed decision-making method is scientific and effective for the decision making of tourist lines of tourist-dedicated train. The decision-making results can be used as a reference for the formulation of tour lines and line plans of tourist-dedicated train.

Object-Oriented Change Detection Method Based on Spectral–Spatial–Saliency Change Information and Fuzzy Integral Decision Fusion for HR Remote Sensing Images

Spectral features in remote sensing images are extensively utilized to detect land cover changes. However, detection noise appearing in the changing maps due to the abundant spatial details in the high-resolution images makes it difficult to acquire an accurate interpretation result. In this paper, an object-oriented change detection approach is proposed which integrates spectral–spatial–saliency change information and fuzzy integral decision fusion for high-resolution remote sensing images with the purpose of eliminating the impact of detection noise. First, to reduce the influence of feature uncertainty, spectral feature change is generated by three independent methods, and spatial change information is obtained by spatial feature set construction and the optimal feature selection strategy. Secondly, the saliency change map of bi-temporal images is obtained with the co-saliency detection method to complement the insufficiency of image features. Then, the image objects are acquired by multi-scale segmentation based on the staking images. Finally, different pixel-level image change information and the segmentation result are fused using the fuzzy integral decision theory to determine the object change probability. Three high-resolution remote sensing image datasets and three comparative experiments were carried out to evaluate the performance of the proposed algorithm. Spectral–spatial–saliency change information was found to play a major role in the change detection of high-resolution remote sensing images, and the fuzzy integral decision strategy was found to effectively obtain reliable changed objects to improve the accuracy and robustness of change detection.

Study on the Characteristics of Surrounding Rock and Design of Backfill Material Parameters for Tunnels Passing through Giant Caverns and Underground Rivers

The influence on tunnel construction of karst and underground rivers is always an important problem in tunnel engineering. In order to demonstrate the rationality of backfill parameters and the effectiveness of supports under the influence of groundwater when a tunnel passes through a large karst cave, the finite element software FLAC3D was used for numerical analysis. Converting the mean values and standard deviations of mechanical response results of the surrounding rock and the supports on multiple sections into the ECULID distance from the origin point on a two-dimensional plane as evaluation indexes, the influence of the reinforcement parameters on the mechanical response of the surrounding rock and the supports was analyzed by orthogonal experiments. Based on fuzzy decision theory, by regarding the ECULID distance between the simulated result of each group and the global optimal value of the multiple evaluation index as the comprehensive evaluation index, a backfill parameter design method was proposed. By comparing the results which used optimal and worst parameters in the FLAC3D, 10 times and 2.5 times differences in longitudinal and horizontal displacement were observed, respectively. Then, the optimal backfill parameters were applied to the actual project for verification. The field monitoring results showed that the optimal backfill parameters can effectively reduce the displacement around the tunnel. After constructing a diversion for the underground river, the water flow in the karst cave did not rise during a rainstorm. This study provides a reference for the design and construction of other projects in the future.

Q-Rung orthopair fuzzy TOPSIS method and the application to information service quality evaluation in online health community

Since the information quality in the online health community is very important for users to obtain valuable health information, information quality evaluation is a necessary research that involves a multi-attribute decision-making (MADM) problem. However, few researches have been done to address both the construction of evaluation criteria and the expression and processing of fuzzy information, especially in online health community. This paper proposes a novel evaluation framework of information service quality combined principal component analysis (PCA) method with the TOPSIS method under q-rung orthopair fuzzy set (q-ROFS) environment. An accurate evaluation criteria system is optimized by the PCA method, and the q-ROF TOPSIS method is proposed to process larger space of fuzzy evaluation information and overcome information loss and information distortion, in which a new distance measure between q-ROFSs is defined and an entropy weight model is initiated to determine the unknown weight of attribute. Moreover, a numerical example is performed to prove the practicability and superiority of the method through comparative analysis, which gives clear results of information quality evaluation of four online health communities. This research ends with fuzzy decision-making theory and application, and provides references for standardizing and improving the information quality of online health communities.

Multicriteria Decision-Making Method and Application in the Setting of Trapezoidal Neutrosophic Z-Numbers

The information expression and modeling of decision-making are critical problems in the fuzzy decision theory and method. However, existing trapezoidal neutrosophic numbers (TrNNs) and neutrosophic Z-numbers (NZNs) and their multicriteria decision-making (MDM) methods reveal their insufficiencies, such as without considering the reliability measures in TrNN and continuous Z-numbers in NZN. To overcome the insufficiencies, it is necessary that one needs to propose trapezoidal neutrosophic Z-numbers (TrNZNs), their aggregation operations, and an MDM method for solving MDM problems with TrNZN information. Hence, this study first proposes a TrNZN set, some basic operations of TrNZNs, and the score and accuracy functions of TrNZN and their ranking laws. Then, the TrNZN weighted arithmetic averaging (TrNZNWAA) and TrNZN weighted geometric averaging (TrNZNWGA) operators are presented based on the operations of TrNZNs. Next, an MDM approach using the proposed aggregation operators and score and accuracy functions is established to carry out MDM problems under the environment of TrNZNs. In the end, the established MDM approach is applied to an MDM example of software selection for revealing its rationality and efficiency in the setting of TrNZNs. The main advantage of this study is that the established approach not only makes assessment information continuous and reliable but also strengthens the decision rationality and efficiency in the setting of TrNZNs.

A weighted fuzzy process neural network model and its application in mixed-process signal classification

A weighted fuzzy process neural network (WFPNN) model, and its corresponding classification algorithm, are proposed for quantitative and qualitative mixed-process signal classification problems. Based on predicate logic, fuzzy process neurons (FPN) are expressed as weighted fuzzy process inference rules on the semantic. Among them, the premise and conclusion are fuzzy predicates defined on a fuzzy set containing process information. Considering the applicability of rules to practical problems, confidence factor and applicable threshold are introduced to make the rules based on FPN have adaptive application ability. Based on FPN, the WFPNN was constructed with a process signal input layer, an FPN hidden layer, and a Takagi-Sugeno fuzzy classifier. It can integrate the learning properties and classification mechanisms of process neural networks (P-NNs) for time-vary signals with the logical inference abilities of fuzzy systems. So then to realize the quantitative and qualitative mixed process signal processing and direct classification. This study proposes a fusion analysis model that combining fuzzy decision theory with the process information processing method of P-NNs. It can automatically deduce the membership function and the fuzzy predicate logic rules from a set of given training examples, quickly build a fuzzy expert system prototype. And it can provide a new method for the construction and application of an expert system based on models or rules. Meanwhile, due to the adaptive learning property of WFPNN and the representation mechanism of inference rules based on the FPN, it is easy to realize the extension of the rule bases, model bases and application fields of the expert systems. This paper analyzes the properties of WFPNN, and establishes a corresponding learning algorithm. The discrimination of reservoir water-flooded states based on well-logging curves was used as an example for experimental analysis. The continuous measurement values of five physical quantities, such as resistivity, acoustic and radioactivity levels in the oil layer, were taken as input. The output was the discriminating result for four types of water-flooded states. The accuracy of the proposed method was 82.5%. Our accuracy was more than 7% higher than other existing automatic identification methods. These results demonstrate the viability and effectiveness of the proposed model and algorithms.

Continuous-Time Co-Operation of Integrated Electricity and Natural Gas Systems With Responsive Demands Under Wind Power Generation Uncertainty

This paper studies the role of electricity demand response program (EDRP) in the co-operation of the electric power systems and the natural gas transmission system to facilitate integration of wind power generation. It is known that time-based uncertainty modeling has a critical role in co-operation of electricity and gas systems. Also, the major limitation of the hourly discrete time model (HDTM) is its inability to handle the fast sub-hourly variations of generation sources. Accordingly, in this paper, this limitation has been solved by the operation of both energy systems with a continuous time model (CTM). Also, a new fuzzy information gap decision theory (IGDT) approach has been proposed to model the uncertainties of the wind energy. Numerical results on the IEEE Reliability Test System (RTS) demonstrate the benefits of applying the continuous-time EDRP to improve the co-scheduling of both natural gas and electricity systems under wind power generation uncertainty.

A Comprehensive Multi-Scenario Routing Algorithm Based on Fuzzy Control Theory in Opportunistic Social Network

With the flourishing of big data and the 5G era, the amount of data to be transmitted in the communication process is increasing, and end-to-end communication in traditional social networks has been unable to meet the current communication needs. Therefore, in order to improve the success rate of data forwarding, social networks propose that the sender of the message should reasonably choose the next hop node. However, existing routing and forwarding algorithms do not take into account nodes that are live in different scenarios, and the applicable next hop node metrics are also different. These algorithms only consider the forwarding preferences of the nodes during working hours and do not consider the forwarding preferences of the nodes during non-working hours. We propose a routing algorithm based on fuzzy decision theory, which aims at a more accurate decision on selecting the next hop. A routing and forwarding algorithm based on fuzzy decision is proposed in this paper. This algorithm symmetrical divides scenes in opportunistic social networks into working time and non-working time according to real human activity. In addition, metrics are designed symmetrically for these two scenarios. Simulation results demonstrate that, in the best case, the proposed scheme presents an average delivery ratio of 0.95 and reduces the average end-to-end delay and average overhead compared with the epidemic routing algorithm, the EIMSTalgorithm, the ICMT algorithm, and the FCNSalgorithm.

PRE-TRAINING ANALYSIS FOR STUDENT’S PLACEMENT TRAINING USING HENDECAGONAL FUZZY NUMBER WITH SIMILARITY MEASURE

Multi-criteria group decision-making approaches with fuzzy theory are trendy in the area of decision science. Frequently decision-makers express their judgment linguistically. This study aims to categorise the student's basic knowledge level in order to provide pre-placement training for the students in the placement. A method is constructed using several linguistic values with different weights using the fuzzy number grading system. An algorithm for fuzzy multi-criteria group decision-making (FMCGDM) is proposed with the notion of hendecagonal fuzzy number (HDFN) and symmetric hendecagonal fuzzy number (symmHDFN). Also, complex proportional assessment (COPRAS) method is used for group decision-making approach, to assess the performance of students in the pre-placement training. Based on the report generated, the college can arrange varying levels of training using fuzzy decision theory.

Fuzzy-logic-based data-differentiated service supported routing protocol for emergency communication networks in underground mines

Hybrid wireless mesh networks are suitable to construct emergency communication networks after disasters in underground mines. The routing decision in emergency scene is more difficult to give an accurate mathematical description due to the constraints of various data types, different data transmission requirements, and multi-parameters. Based on the fuzzy decision theory, this article has proposed a fuzzy-logic-based data-differentiated service supported routing protocol. Through the use of the adaptive fuzzy decision system, fuzzy-logic-based data-differentiated service supported routing protocol can provide data-differentiated services and make optimized routing decisions to satisfy the transmission requirements of different data types. In addition, a path soft handoff strategy has been proposed to maintain continuous data transmission when the path quality deteriorates. Based on NS2, we set three transmission scenarios (transmitting emergency data, regular data, or mixed data) to test the performances of fuzzy-logic-based data-differentiated service supported routing protocol, ad hoc on-demand distance vector, FUZZY-ad hoc on-demand distance vector, and multi-criteria routing metric. The results show that the fuzzy-logic-based data-differentiated service supported routing protocol has a higher delivery ratio and lower end-to-end delay when transmitting emergency data. When transmitting regular data, fuzzy-logic-based data-differentiated service supported routing protocol has achieved higher throughput and longer network lifetime than that of similar algorithms.

Optimization of Production Equipment Layout Based on Fuzzy Decision and Evolutionary Algorithm

A method of optimizing equipment layout is proposed in production equipment layout of a complex manufacturing system, using fuzzy decisions combined with evolutionary algorithms. First, the optimization model is improved, the total cost is minimized, while the requirements of adjacent equipment and the space utilization are maximized; the material handling cost, the resetting cost, the loss of production costs are considered in the total cost objective. Second, this method takes into account the ambiguity of the satisfaction and priority of users such as cost, utilization and proximity requirements, based on the fuzzy decision theory, the multi-objective optimization model is fuzzified, and the fuzzy fitness function is designed to evaluate the pareto solution set according to the user's priority relation. Based on the characteristics of the solution model, the chromosomal coding method of multi-objective evolutionary algorithms and the genetic operation mode are improved, and the practicability and efficiency of the model is improved. Finally, the effectiveness of the method is proved by the optimization of the practical case.