Showing papers in "IEEE Transactions on Fuzzy Systems in 2023"
TL;DR: In this paper , an improved finite-time performance function (FTPF) for a fuzzy fault-tolerant distributed cooperative control scheme was proposed to achieve finite time robust precision bipartite consensus tracking (BCT) tasks for nonlinear multiagent systems.
Abstract: In bipartite consensus tracking (BCT) tasks for nonlinear multiagent systems (MASs), stochastic disturbances and actuator faults are regarded as essential factors that hamper effective controller formulation and tracking precision improvement. To address these difficulties, we design an improved finite-time performance function (FTPF) for a fuzzy fault-tolerant distributed cooperative control scheme to achieve finite-time robust precision BCT tasks for nonlinear MASs. The parameter selection range of the improved FTPF is relaxed, which renders systems to achieve better transient performance. Benefitting from the stochastic Lyapunov stability theory, it is shown that all signals of systems are semiglobal uniformly ultimately bounded in probability, and bipartite consensus errors can satisfy the arbitrary precision with probability in the predefined time. Finally, to verify its effectiveness, the proposed control scheme is applied to BCT tasks of a group of vehicles, which manifests anticipated control performance under various uncertainties.
42 citations
20 citations
TL;DR: Wang et al. as mentioned in this paper proposed a hybrid decision-making framework for prioritizing sustainable public transportation in Metaverse under q-rung orthopair fuzzy set (q-ROFS) context.
Abstract: Blockchain technology and metaverse advancements allow people to create virtual personalities and spend time online. Integrating public transportation into the metaverse could improve services and collect user data. This article introduces a hybrid decision-making framework for prioritizing sustainable public transportation in Metaverse under q-rung orthopair fuzzy set (q-ROFS) context. In this regard, first, q-rung orthopair fuzzy (q-ROF) generalized Dombi weighted aggregation operators and their characteristics are developed to aggregate the q-ROF information. Second, a q-ROF information-based method using the removal effects of criteria and stepwise weight assessment ratio analysis models are proposed to find the objective and subjective weights of criteria, respectively. Then, a combined weighting model is taken to determine the final weights of the criteria. Third, the weighted sum product method is extended to q-ROFS context by considering the double normalization procedures, the proposed operators and integrated weighting model. This method has taken the advantages of two normalization processes and four utility measures that approve the effect of benefit and cost criteria by using weighted sum and weighted product models. Next, to demonstrate the practicality and effectiveness of the presented method, a case study of sustainable public transportation in metaverse is presented in the context of q-ROFSs. The findings of this article confirms that the proposed model can recommend more feasible performance while facing numerous influencing factors and input uncertainties, and thus, provides a wider range of applications.
18 citations
TL;DR: In this article , an improved finite-time performance function (FTPF) for a fuzzy fault-tolerant distributed cooperative control scheme was proposed to achieve finite time robust precision bipartite consensus tracking (BCT) tasks for nonlinear multiagent systems.
Abstract: In bipartite consensus tracking (BCT) tasks for nonlinear multiagent systems (MASs), stochastic disturbances and actuator faults are regarded as essential factors that hamper effective controller formulation and tracking precision improvement. To address these difficulties, we design an improved finite-time performance function (FTPF) for a fuzzy fault-tolerant distributed cooperative control scheme to achieve finite-time robust precision BCT tasks for nonlinear MASs. The parameter selection range of the improved FTPF is relaxed, which renders systems to achieve better transient performance. Benefitting from the stochastic Lyapunov stability theory, it is shown that all signals of systems are semiglobal uniformly ultimately bounded in probability, and bipartite consensus errors can satisfy the arbitrary precision with probability in the predefined time. Finally, to verify its effectiveness, the proposed control scheme is applied to BCT tasks of a group of vehicles, which manifests anticipated control performance under various uncertainties.
17 citations
TL;DR: Wang et al. as mentioned in this paper proposed a hybrid decision-making framework for prioritizing sustainable public transportation in Metaverse under q-rung orthopair fuzzy set (q-ROFS) context.
Abstract: Blockchain technology and metaverse advancements allow people to create virtual personalities and spend time online. Integrating public transportation into the metaverse could improve services and collect user data. This article introduces a hybrid decision-making framework for prioritizing sustainable public transportation in Metaverse under q-rung orthopair fuzzy set (q-ROFS) context. In this regard, first, q-rung orthopair fuzzy (q-ROF) generalized Dombi weighted aggregation operators and their characteristics are developed to aggregate the q-ROF information. Second, a q-ROF information-based method using the removal effects of criteria and stepwise weight assessment ratio analysis models are proposed to find the objective and subjective weights of criteria, respectively. Then, a combined weighting model is taken to determine the final weights of the criteria. Third, the weighted sum product method is extended to q-ROFS context by considering the double normalization procedures, the proposed operators and integrated weighting model. This method has taken the advantages of two normalization processes and four utility measures that approve the effect of benefit and cost criteria by using weighted sum and weighted product models. Next, to demonstrate the practicality and effectiveness of the presented method, a case study of sustainable public transportation in metaverse is presented in the context of q-ROFSs. The findings of this article confirms that the proposed model can recommend more feasible performance while facing numerous influencing factors and input uncertainties, and thus, provides a wider range of applications.
16 citations
TL;DR: In this paper , an adaptive fixed-time fault-tolerant tracking fuzzy control issue for nonlinear switched systems with dynamic uncertainties is investigated, where the backstepping method and fuzzy logic estimator are employed.
Abstract: This article investigates the adaptive fixed-time fault-tolerant tracking fuzzy control issue for nonlinear switched systems with dynamic uncertainties. Actuator faults considered in this article simultaneously contain the loss of effectiveness and time-varying bias fault depending on the switching signal. The generated scheme extends the fixed-time convergence to switched nonlinear systems with unmodeled dynamics. An improved adaptive fixed-time fault-tolerant controller is proposed by employing the backstepping method and fuzzy logic estimator. In particular, the presented framework removes the singularity, and the convergence time is assignable for any initial condition. Finally, a numerical simulation example and a resistor-capacitor-inductor circuit system example are given to prove the system output can converge to a desired trajectory within a fixed time.
15 citations
TL;DR: Wang et al. as discussed by the authors proposed a two-way CCL (TCCL) method for dynamic concept learning in a fuzzy context, which is more flexible and less time consuming to learn granule concepts.
Abstract: Granular computing (GrC) and two-way concept learning (TCL) are influential studies of knowledge processing and cognitive learning. A central notion of two-way concept learning is learning concepts from an arbitrary information granule. Although TCL has been widely adopted for concept learning and formal concept analysis in a fuzzy context, the existing studies of TCL still have some issues: the sufficient and necessary granule concept is only obtained from the necessary granule or sufficient granule concept; and the cognitive mechanism ignores integrating past experiences into itself to deal with dynamic data. Meanwhile, concept-cognitive learning (CCL) method still faces challenges, such as incomplete cognitive and weak generalization ability. This article proposes a novel two-way CCL (TCCL) method for dynamic concept learning in a fuzzy context for these problems and challenges. Unlike TCL, fuzzy-based TCCL (F-TCCL) is more flexible and less time consuming to learn granule concepts from the given clue, and meanwhile, it is good at dynamic concept learning. Moreover, we design a fuzzy-based progressive learning mechanism within this framework under the dynamic environment. Some numerical experiments on public datasets verify the effectiveness of our proposed method. The considered framework can provide a convenient novel method for researching two-way learning and CCL.
15 citations
TL;DR: In this article , an adaptive fixed-time fault-tolerant tracking fuzzy control issue for nonlinear switched systems with dynamic uncertainties is investigated, where the backstepping method and fuzzy logic estimator are employed.
Abstract: This article investigates the adaptive fixed-time fault-tolerant tracking fuzzy control issue for nonlinear switched systems with dynamic uncertainties. Actuator faults considered in this article simultaneously contain the loss of effectiveness and time-varying bias fault depending on the switching signal. The generated scheme extends the fixed-time convergence to switched nonlinear systems with unmodeled dynamics. An improved adaptive fixed-time fault-tolerant controller is proposed by employing the backstepping method and fuzzy logic estimator. In particular, the presented framework removes the singularity, and the convergence time is assignable for any initial condition. Finally, a numerical simulation example and a resistor-capacitor-inductor circuit system example are given to prove the system output can converge to a desired trajectory within a fixed time.
14 citations
TL;DR: In this article , the authors proposed a novel benchmarking of oil companies by extending the linear Diophantine fuzzy rough sets (LDFRSs) into the complex multicriteria decision making (MCDM) methods to help build a sustainable transportation industry.
Abstract: Building a sustainable transportation system without involving international oil companies (IOCs) is an unrealistic feat. To date, no study has determined the best IOC and low-performing ones with respect to sustainable oil transportation, which is considered a benchmarking challenge requiring an urgent solution. Despite this limitation, the benchmarking of IOCs falls under the complex multicriteria decision making (MCDM) because of the use of several evaluation criteria and their varying datasets and the varying importance of these criteria. Moreover, the issues involving the use of imprecise, unclear, and ambiguous information remain unresolved in the existing multiattribute decision-making methods. The robustness of the multiobjective optimization on the basis of ratio analysis (MULTIMOORA, i.e., an updated version of MOORA) plus full-multiplicative form method and that of the fuzzy-weighted with zero inconsistency (FWZIC) method have been proven. Therefore, in this article, we propose a novel benchmarking of oil companies by extending the linear Diophantine fuzzy rough sets (LDFRSs) into the MCDM methods to help build a sustainable transportation industry. The proposed methodology consists of two phases. The initial phase involves assigning values to the evaluation criteria of IOCs to formulate the evaluation decision matrix. The second phase involves the development of two fuzzy MCDM methods, namely, the LDFRS with the FWZIC method (hereafter called LDFRS–FWZIC) for weighting the criterion of each IOC and the LDFRS with the MULTIMOORA method (hereafter called LDFRS–MULTIMOORA) for benchmarking the IOCs. The IOCs were evaluated based on 2 criteria, 9 subcriteria, and 47 measurement items by 483 experts from 11 IOCs. Results revealed the following: 1) LDFRS–FWZIC can effectively weigh the evaluation criteria of IOCs. The highest final weight of 0.2594 was for “cost leadership” (C2-1), whereas the lowest weights of 0.1148 was for “priority of other external matters” (C1-2) and “insufficient supply”(C1-4), and 2) LDFRS–MULTIMOORA can successfully benchmark the IOCs. IOC11 ranked first, followed by IOC10 and IOC3 in the second and third ranks, respectively. IOC4 ranked the lowest (rank = 11 ). A sensitivity analysis was conducted to determine the robustness of the developed fuzzy MCDM methods.
13 citations
TL;DR: In this paper , the authors proposed a novel benchmarking of oil companies by extending the linear Diophantine fuzzy rough sets (LDFRSs) into the complex multicriteria decision making (MCDM) methods to help build a sustainable transportation industry.
Abstract: Building a sustainable transportation system without involving international oil companies (IOCs) is an unrealistic feat. To date, no study has determined the best IOC and low-performing ones with respect to sustainable oil transportation, which is considered a benchmarking challenge requiring an urgent solution. Despite this limitation, the benchmarking of IOCs falls under the complex multicriteria decision making (MCDM) because of the use of several evaluation criteria and their varying datasets and the varying importance of these criteria. Moreover, the issues involving the use of imprecise, unclear, and ambiguous information remain unresolved in the existing multiattribute decision-making methods. The robustness of the multiobjective optimization on the basis of ratio analysis (MULTIMOORA, i.e., an updated version of MOORA) plus full-multiplicative form method and that of the fuzzy-weighted with zero inconsistency (FWZIC) method have been proven. Therefore, in this article, we propose a novel benchmarking of oil companies by extending the linear Diophantine fuzzy rough sets (LDFRSs) into the MCDM methods to help build a sustainable transportation industry. The proposed methodology consists of two phases. The initial phase involves assigning values to the evaluation criteria of IOCs to formulate the evaluation decision matrix. The second phase involves the development of two fuzzy MCDM methods, namely, the LDFRS with the FWZIC method (hereafter called LDFRS–FWZIC) for weighting the criterion of each IOC and the LDFRS with the MULTIMOORA method (hereafter called LDFRS–MULTIMOORA) for benchmarking the IOCs. The IOCs were evaluated based on 2 criteria, 9 subcriteria, and 47 measurement items by 483 experts from 11 IOCs. Results revealed the following: 1) LDFRS–FWZIC can effectively weigh the evaluation criteria of IOCs. The highest final weight of 0.2594 was for “cost leadership” (C2-1), whereas the lowest weights of 0.1148 was for “priority of other external matters” (C1-2) and “insufficient supply”(C1-4), and 2) LDFRS–MULTIMOORA can successfully benchmark the IOCs. IOC11 ranked first, followed by IOC10 and IOC3 in the second and third ranks, respectively. IOC4 ranked the lowest (rank = 11). A sensitivity analysis was conducted to determine the robustness of the developed fuzzy MCDM methods.
13 citations
TL;DR: Wang et al. as discussed by the authors proposed an overlapping community driven feedback mechanism for improving consensus in social network group decision-making (SN-GDM) which enables the inconsistent subgroups to minimize the consensus cost by selecting personalized feedback parameters.
Abstract: Social network group decision-making (SN-GDM) provides valuable support for obtaining agreed decision results by effectively utilizing the connected social trust relationships among individuals. However, the impact of intricately overlapped social trust relationships within overlapping communities on evaluation modifications in the SN-GDM consensus reaching process is seldom considered. To alleviate this issue, this study attempts to construct an overlapping community driven feedback mechanism for improving consensus in SN-GDM. The Lancichinetti-Fortunato method (LFM) is used to detect the overlapping community structures under social trust networks. Subsequently, the trusted recommendation advice is conducted within overlapping communities, which guides the inconsistent subgroups to make an interaction with each other to reach higher consensus level. Then, an associated feedback mechanism for SN-GDM with overlapping communities is proposed, which enables the inconsistent subgroups to minimize the consensus cost by selecting personalized feedback parameters. Moreover, it shows that the overlapping communities based feedback mechanism is superior to the feedback mechanisms with non-overlapping communities. Finally, an illustrative example is included, which is also used to testify the efficacy of proposal by comparing the consensus cost under different representative recommendation advice in overlapping social trust networks.
TL;DR: In this article , the design of fuzzy synchronization controller of Sugeno (T-S) fuzzy neural networks with distributed time-varying delay and probabilistic network communication delay is considered.
Abstract: This article studies the design of fuzzy synchronization controller of Takagi—Sugeno (T-S) fuzzy neural networks with distributed time-varying delay and probabilistic network communication delay. Compared with the existing model of neural networks with distributed time-varying delay without kernel, a more general model containing a distributed delay kernel is considered. By utilizing the probability distribution of random communication delays, a distributed but deterministic delay model is established. In this model, the delay probability density function is treated as the distributed delay kernel. By developing a new Lyapunov-Krasovskii functional (LKF) related to the distributed delay kernels and using an integral inequality, new sufficient conditions for the existence of a synchronization controller are presented. Finally, a numerical simulation and an application of encrypting the image are carried out to illustrate the effectiveness of the developed strategy.
TL;DR: Based on regret theory, this article established a generalized three-way decision method on incomplete multiscale decision information systems with interval fuzzy numbers, which can still maintain more than 97% consistency in an incomplete information system with a missing rate of at most 20%.
Abstract: In realistic decision-making environments, human behaviors bring influences to various decision-making procedures, and classic multiattribute decision-making approaches based on utility decisions own some deviations from actual situations. The behavioral decision theory modifies classic decision-making theories to make the new method more applicable. Regret theory, as one of the important components in behavioral decision theories, has been widely used in theories and applications. Based on regret theory, we establish a generalized three-way decision method on incomplete multiscale decision information systems with interval fuzzy numbers. First, we select an incomplete optimal subsystem for the incomplete multiscale information system, and convert the multiscale evaluation information into an interval fuzzy number by using a linguistic term set. Second, based on the probability distribution of evaluation values and tradeoff factors, we propose a target-dependent approximation estimation method for the incomplete interval fuzzy subsystem. Then, the regret–rejoicing preferences between objects are obtained. Finally, a tripartition and ranking method based on a max-bipartition and preference index is established. Moreover, the incompleteness experiments show that the decision-making results of our method can still maintain more than 97% consistency in an incomplete information system with a missing rate of at most 20%. In addition, the parameter analysis shows that the proposed method maintains an accuracy rate of more than 98% and a classification error rate of no more than 0.6%.
TL;DR: In this article , an improved Lyapunov-Krasovskii functions (LKFs) based on fuzzy membership functions are constructed, which combine the characteristics of nonlinear problems in the system to reduce the initial constraints.
Abstract: This article analyzes the mechanism of denial-of-service (DoS) attacks initiated by hackers from the perspective of computer networks. In order to effectively estimate the performance error generated by the T–S fuzzy networked control systems under DoS attacks, we transform the performance error estimation problem into the one of finding ellipsoid constraints $\mathfrak {J}(P_{i})$. First, improved Lyapunov–Krasovskii functions (LKFs) based on fuzzy membership functions are constructed, which combine the characteristics of nonlinear problems in the system to reduce the initial constraints. Then, a second-order weight method (SOWM) is introduced to divide the time interval of sampling meticulously. Besides, the information stored in the LKFs is enhanced. Furthermore, we construct the novel looped functions by relying on the SOWM. Next, a suitable integral elastic event trigger mechanism is established to ensure that the performance errors caused by the attacks are estimated. Finally, the feasibility of the proposed method is verified by a two-degree-of-freedom helicopter system.
TL;DR: In this article , the authors presented a pavement strategy selection based on a new extension of fuzzy MCDM methods, which is developed in two phases: first, the evaluation decision matrix is formulated on the basis of intersecting the “evaluation criteria” and “pavement strategies list.
Abstract: Pavement strategy is critical for achieving sustainable transportation. However, the presence of many evaluation criteria, criteria tradeoffs, criteria conflict, and criteria importance categorize the evaluation and selection of pavement strategies under complex multicriteria decision-making (MCDM) problems. To date, no study has presented an evaluation framework for selecting the most optimal pavement strategy to be utilized as a way to achieve sustainable transportation considering multicriteria evaluation of pavement strategies and sustainable solutions. This article presents a pavement strategy selection based on a new extension of fuzzy MCDM methods. The methodology is developed in two phases. First, the evaluation decision matrix is formulated on the basis of intersecting the “evaluation criteria” and “pavement strategies list.” Second, the proposed MCDM methods are developed: multilayer dual hesitant fuzzy weighted zero inconsistency (DH-FWZIC) to assign weights to the pavement's evaluation criteria followed by dual hesitant fuzzy decision by opinion score method (DH-FDOSM) for selecting the best pavement strategy. Four alternatives, namely, flexible/asphalt, rigid/concrete, reflective, and permeable alternatives pavement strategies, are evaluated on the basis of 30 criteria. Results show the following. 1) The multilayer DH-FWZIC method has weighted the pavement strategies’ evaluation criteria at three layers in a consistent manner, showing that a region's environment criterion has the highest final weight (0.1215) and the lowest importance value (0.0089) assigned for its windy criterion. 2) According to DH-FDOSM, the flexible/asphalt pavement achieved the first rank among the four pavement strategies. Finally, the robustness of the developed framework was assessed by conducting sensitivity analysis and comparison study.
TL;DR: In this paper , the control design of adaptive fuzzy fixed-time high-order sliding mode (HOSM) under asymmetric output constraints is studied. But the main objective is to construct a control scheme to build the HOSM in a fixed time and fulfill the pre-set constraint condition.
Abstract: This paper is concerned with the control design of adaptive fuzzy fixed-time high-order sliding mode (HOSM) under asymmetric output constraints. The main objective of the paper is to construct a control scheme to build the HOSM in a fixed time and fulfill the pre-set constraint condition. For this purpose, initially to conquer this obstacle regarding the output constraint, a barrier Lyapunov function is developed to keep the output variable within the predefined constraint during operation. Then, the unknown functional bounds of uncertainties in the considered system are modeled by invoking fuzzy logic systems. With the integrated use of adaptive fuzzy control and backstepping-like techniques, a novel protocol for the fixed-time HOSM control with asymmetric output constraints is developed. Through the strictly theoretical clarification, the HOSM is fixed-time established under the suggested control strategy. The effectiveness of the derived results is eventually testified by some comparative simulation results.
TL;DR: In this paper , a concise fuzzy neural control framework was proposed for waverider vehicles with input constraints, while the prescribed performance can be guaranteed, and the challenging fragility problem associated with the existing prescribed performance control (PPC) is avoided.
Abstract: In this article, we propose a concise fuzzy neural control framework for waverider vehicles with input constraints, while the spurred prescribed performance can be guaranteed, and the challenging fragility problem associated with the existing prescribed performance control (PPC) is avoided. Unlike the existing control protocols without considering computational costs, in this study, the low-complexity fuzzy neural approximation is combined with simple performance functions, which reduces the complexity burden and improves the practicability. Then, in order to handle the adverse effect of the actuator saturation on the control performance, bounded-input-bounded-state stable systems are developed to stabilize the closed-loop control system based on bounded compensations. Specially, flexible adjustment terms are exploited to modify the developed simple performance functions, while fragility-free prescribed performance is achieved for tracking errors, and moreover the fragility defect of the existing PPC is remedied. Finally, the efficiency and superiority of the design are verified via compared simulations.
TL;DR: In this paper , the design of fuzzy synchronization controller of Takagi-Sugeno (T-S) fuzzy neural networks with distributed time-varying delay and probabilistic network communication delay is considered.
Abstract: In this article, we study the design of fuzzy synchronization controller of Takagi–Sugeno (T–S) fuzzy neural networks with distributed time-varying delay and probabilistic network communication delay. Compared with the existing model of neural networks with distributed time-varying delay without kernel, a more general model containing a distributed delay kernel is considered. By utilizing the probability distribution of random communication delays, a distributed but deterministic delay model is established. In this model, the delay probability density function is treated as the distributed delay kernel. By developing a new Lyapunov–Krasovskii functional related to the distributed delay kernels and using an integral inequality, new sufficient conditions for the existence of a synchronization controller are presented. Finally, a numerical simulation and an application of encrypting the image are carried out to illustrate the effectiveness of the developed strategy.
TL;DR: In this article , a novel approximate estimation method for incomplete utility values via the regret theory was proposed, which can be used to calculate the utility difference and regret-rejoicing values for pairwise comparisons.
Abstract: The three-way decision theory provides a three-way philosophical thinking to solve problems, and the regret theory quantifies the risk preferences of decision makers under different psychological behaviors. On the one hand, the combination of these two theories makes models more practical by considering the psychological behaviors of decision makers. On the other hand, we can effectively combine the advantages of the three-way decision theory with the regret theory to highlight the interpretability of decision making processes. In this paper, we propose a novel approximate estimation method for incomplete utility values via the regret theory and establish a wide sense of three-way decision model on incomplete multi-scale decision information systems. First, the degree of consistency for each scale is measured via using the dependency degree, then the optimal subsystem is selected by evaluating the scale selection cost. Further, the incomplete multi-scale evaluation information is transformed into triangular fuzzy numbers via linguistic term sets. Second, in light of fuzzy evaluation values and trade-off factors, an estimation method for incomplete fuzzy subsystems is constructed, which can be used to calculate the utility difference and regret-rejoicing values for pairwise comparisons. Finally, from the perspective of human cognition, the tri-partition and the corresponding decision rules are built by the tolerance degree, and the ranking of objects is calculated by the relative closeness degree. Additionally, multi-aspect comparative and experimental analyses are performed by extensive experiments, and the feasibility, validity and stability of the constructed model are shown by parametric analyses.
TL;DR: Based on regret theory, this paper established a generalized three-way decision method on incomplete multiscale decision information systems with interval fuzzy numbers, and the regret-rejoicing preferences between objects were obtained.
Abstract: In realistic decision-making environments, human behaviors bring influences to various decision-making procedures, and classic multiattribute decision-making approaches based on utility decisions own some deviations from actual situations. The behavioral decision theory modifies classic decision-making theories to make the new method more applicable. Regret theory, as one of the important components in behavioral decision theories, has been widely used in theories and applications. Based on regret theory, we establish a generalized three-way decision method on incomplete multiscale decision information systems with interval fuzzy numbers. First, we select an incomplete optimal subsystem for the incomplete multiscale information system, and convert the multiscale evaluation information into an interval fuzzy number by using a linguistic term set. Second, based on the probability distribution of evaluation values and tradeoff factors, we propose a target-dependent approximation estimation method for the incomplete interval fuzzy subsystem. Then, the regret–rejoicing preferences between objects are obtained. Finally, a tripartition and ranking method based on a max-bipartition and preference index is established. Moreover, the incompleteness experiments show that the decision-making results of our method can still maintain more than 97% consistency in an incomplete information system with a missing rate of at most 20%. In addition, the parameter analysis shows that the proposed method maintains an accuracy rate of more than 98% and a classification error rate of no more than 0.6%.
TL;DR: In this paper , a multicriteria group decision-making methodology with dual hesitant fuzzy (DHF) sets is presented to evaluate zero-emission last-mile delivery (LMD) solutions for sustainable city logistics.
Abstract: For the first time, the critical worldwide problem of prioritizing zero-emission last-mile delivery (LMD) solutions for sustainable city logistics is addressed and solved in this article. It not only aims to help city logistics companies sustainably decarbonize urban freight distribution but also provide valuable practical guidelines. To evaluate zero-emission LMD solutions, this article presents a novel multicriteria group decision-making methodology with dual hesitant fuzzy (DHF) sets. First, we propose some improved operations on DHF elements and investigate their vital properties. Second, based on these operations, we develop DHF improved weighted averaging operator to overcome the drawbacks of the existing operators on DHF sets. Third, for measuring the weights of criteria, a new model called the cross-entropy-based optimization model (CEBOM) is developed. Fourth, for the rational aggregation of the preferences, we formulate a new method namely score-based double normalized measurement alternatives and ranking according to the compromise solution (SDNMARCOS). The proposed DNMARCOS method couples the linear and vector normalization techniques. It is composed of the complete compensatory model and the incomplete compensatory model. Thus, SDNMARCOS is more robust compared to the available state-of-the-art approaches. To exhibit the applicability of the proposed DHF-CEBOM-SDNMARCOS methodology in real-world settings, a case study for one of the largest Austrian logistics companies in Serbia is provided. The research findings show that electric light commercial vehicles are the best LMD solution. Also, it is recommended to consider electric cargo bikes as a viable mid-term solution. The superiority of the introduced methodology is demonstrated through the comparative investigation.
TL;DR: Wang et al. as mentioned in this paper proposed a two-way CCL (TCCL) method for dynamic concept learning in a fuzzy context, which is more flexible and less time consuming to learn granule concepts.
Abstract: Granular computing (GrC) and two-way concept learning (TCL) are influential studies of knowledge processing and cognitive learning. A central notion of two-way concept learning is learning concepts from an arbitrary information granule. Although TCL has been widely adopted for concept learning and formal concept analysis in a fuzzy context, the existing studies of TCL still have some issues: the sufficient and necessary granule concept is only obtained from the necessary granule or sufficient granule concept; and the cognitive mechanism ignores integrating past experiences into itself to deal with dynamic data. Meanwhile, concept-cognitive learning (CCL) method still faces challenges, such as incomplete cognitive and weak generalization ability. This article proposes a novel two-way CCL (TCCL) method for dynamic concept learning in a fuzzy context for these problems and challenges. Unlike TCL, fuzzy-based TCCL (F-TCCL) is more flexible and less time consuming to learn granule concepts from the given clue, and meanwhile, it is good at dynamic concept learning. Moreover, we design a fuzzy-based progressive learning mechanism within this framework under the dynamic environment. Some numerical experiments on public datasets verify the effectiveness of our proposed method. The considered framework can provide a convenient novel method for researching two-way learning and CCL.
TL;DR: In this paper , the authors proposed a new multiobjective optimization approach for designing a self-generated interpretable fuzzy logic system (FLS), where the types of fuzzy sets can be constructed automatically by self-organizing method, so as to form a hybrid fuzzy system.
Abstract: This paper proposes a new multiobjective optimization approach for designing a self-generated interpretable fuzzy logic system (FLS). The types of fuzzy sets can be constructed automatically by self-organizing method, so as to form a hybrid fuzzy system. Different from the existing evolutionary type-1 fuzzy system, which is full of type-1 fuzzy sets, and the evolutionary interval type-2 fuzzy system, which is full of interval type-2 fuzzy sets, there are both type-1 fuzzy sets and interval type-2 fuzzy sets in the hybrid fuzzy system. A new transparency-oriented objective function is defined, and the constraint of the footprint of uncertainty (FOU) of the interval type-2 (IT2) fuzzy set (FS) is considered for the first time. A new FS merging criterion focusing on the proximity of the cores of fuzzy sets is proposed, which is easy to calculate and maintains the characteristics of classical similarity measures. Combined with the new merging criterion, the online cluster and fuzzy set updating (OCFU) algorithm is employed to initialize the reference rule base and the type of fuzzy sets, as it is assumed that no training data are collected in advance. Based on the reference rule base, the advanced multiobjective front-guided continuous ant colony optimization (AMO-FCACO) algorithm is introduced to optimize all the free parameters of the FLS. With the operation mentioned above, the self-generated FLSs achieve a good balance between interpretability and performance. The effectiveness of the proposed method is verified by three nonlinear system tracking problems.
TL;DR: In this paper , a fuzzy neural control framework for Waverider Vehicles with input constraints is proposed, while the prescribed performance can be guaranteed, and the challenging fragility problem associated with the existing prescribed performance control (PPC) is avoided.
Abstract: In this article, we propose a concise fuzzy neural control framework for Waverider Vehicles with input constraints, while the spurred prescribed performance can be guaranteed, and the challenging fragility problem associated with the existing prescribed performance control (PPC) is avoided. Unlike the existing control protocols without considering computational costs, in this study, the low-complexity fuzzy neural approximation is combined with simple performance functions, which reduces the complexity burden and improves the practicability. Then, in order to handle the adverse effect of the actuator saturation on the control performance, bounded-input-bounded-state stable systems are developed to stabilize the closed-loop control system based on bounded compensations. Specially, flexible adjustment terms are exploited to modify the developed simple performance functions, while fragility-free prescribed performance is achieved for tracking errors, and moreover the fragility defect of the existing PPC is remedied. Finally, the efficiency and superiority of the design are verified via compared simulations.
TL;DR: In this article , a two-stage process of obtaining DMs' opinions based on opinion dynamics is developed to analyze the interaction of DMs before clustering, and a new method for determining the heterogeneous weights of the DMs and subgroups is discussed.
Abstract: Compared with group decision making (GDM), it is more difficult to reach a consensus in large-scale group decision making (LSGDM) owing to the large number of decision makers (DMs). Moreover, studies on LSGDM under social trust networks are obviously fewer than those on GDM, and rarely utilize opinion dynamics to analyze the interaction of DMs. Therefore, in the context of multi-criteria large-scale group decision making (MCLSGDM), an MCLSGDM consensus decision framework and a bounded confidence-based consensus optimization model are proposed. First, a trust propagation method considering the relative importance of the trust degrees (TDs) is proposed. Then, a two-stage process of obtaining DMs’ opinions based on opinion dynamics is developed to analyze the interaction of DMs before clustering. Furthermore, a new method for determining the heterogeneous weights of DMs and subgroups is discussed. Finally, to consider the adjustment willingness of DMs, this study proposes a two-stage optimization consensus model based on bounded confidence. In addition, a numerical example is used to further elaborate the above methods and models, and highlight their rationality and superiority through a series of simulation experiments and comparative analysis.
TL;DR: In this article , a probabilistic linguistic three-way decision (TWD) method based on the regret theory (RT), namely PL-TWDR, was proposed for multi-attribute decision-making (MADM), and considering the effective rationality of a decision-maker (DM) in complex decision environments.
Abstract: Aiming at multi-attribute decision-making (MADM) problems with probabilistic linguistic term sets (PLTSs), and considering the effective rationality of a decision-maker (DM) in complex decision environments, this paper proposes a probabilistic linguistic three-way decision (TWD) method based on the regret theory (RT), namely PL-TWDR. First, a probabilistic linguistic attribute weight determination method is developed that considers probabilistic linguistic information entropies and the weighted total deviation of all objects from the negative ideal solution (NIS). Then, a new group satisfaction index is designed to replace the utility function in RT, which overcomes the limitation of the RT calculation in PLTSs. Second, the fuzzy c-means (FCM) algorithm is extended to PLTSs for obtaining equivalent objects under different clusters and calculate conditional probabilities in corresponding TWD models, which makes up for the shortage of the PLTS evaluation matrix when dividing equivalence classes. Third, RT is introduced into PLTSs to rank objects according to utility perception values. At the same time, a new TWD model constructed by average utility perception values is used to realize object domains in probabilistic linguistic environments. Finally, the proposed method is applied to realistic cases, and the effectiveness and superiority of the PL-TWDR method are verified via comparative analysis and sensitivity analysis in terms of other nine popular decision-making methods.
TL;DR: Wang et al. as discussed by the authors proposed a feature grouping and selection approach in robust fuzzy rough approximation space using graph theory (FGS-RFRAS) to enhance the robustness and antinoise ability of the fuzzy rough set model.
Abstract: Most extant feature selection works neglect interactive features in the form of groups, leading to the omission of some important discriminative information. Moreover, the prevalence of data with uncertainty, fuzziness, and noise poses a certain obstacle to feature selection. Driven by these two issues, a Feature Grouping and Selection approach in Robust Fuzzy Rough Approximation Space using graph theory (FGS-RFRAS) is proposed in this study. First, a robust fuzzy rough approximation space is constructed by a neighborhood adaptive $\beta$ Interactive Retainment, Weakly Correlated Removal, and Max-Dependent Selection is devised to guide feature grouping and selection. Experiments are performed on 21 datasets to evaluate the performance of FGS-RFRAS and demonstrate its significance. The robustness test indicates that it is antinoise for mislabeling.
TL;DR: In this article , a multicriteria group decision-making methodology with dual hesitant fuzzy (DHF) sets is presented to evaluate zero-emission last-mile delivery (LMD) solutions for sustainable city logistics.
Abstract: For the first time, the critical worldwide problem of prioritizing zero-emission last-mile delivery (LMD) solutions for sustainable city logistics is addressed and solved in this article. It not only aims to help city logistics companies sustainably decarbonize urban freight distribution but also provide valuable practical guidelines. To evaluate zero-emission LMD solutions, this article presents a novel multicriteria group decision-making methodology with dual hesitant fuzzy (DHF) sets. First, we propose some improved operations on DHF elements and investigate their vital properties. Second, based on these operations, we develop DHF improved weighted averaging operator to overcome the drawbacks of the existing operators on DHF sets. Third, for measuring the weights of criteria, a new model called the cross-entropy-based optimization model (CEBOM) is developed. Fourth, for the rational aggregation of the preferences, we formulate a new method namely score-based double normalized measurement alternatives and ranking according to the compromise solution (SDNMARCOS). The proposed DNMARCOS method couples the linear and vector normalization techniques. It is composed of the complete compensatory model and the incomplete compensatory model. Thus, SDNMARCOS is more robust compared to the available state-of-the-art approaches. To exhibit the applicability of the proposed DHF-CEBOM-SDNMARCOS methodology in real-world settings, a case study for one of the largest Austrian logistics companies in Serbia is provided. The research findings show that electric light commercial vehicles are the best LMD solution. Also, it is recommended to consider electric cargo bikes as a viable mid-term solution. The superiority of the introduced methodology is demonstrated through the comparative investigation.
TL;DR: In this article , the authors presented a pavement strategy selection based on a new extension of fuzzy MCDM methods, which is developed in two phases: first, the evaluation decision matrix is formulated on the basis of intersecting the “evaluation criteria” and “pavement strategies list.
Abstract: Pavement strategy is critical for achieving sustainable transportation. However, the presence of many evaluation criteria, criteria tradeoffs, criteria conflict, and criteria importance categorize the evaluation and selection of pavement strategies under complex multicriteria decision-making (MCDM) problems. To date, no study has presented an evaluation framework for selecting the most optimal pavement strategy to be utilized as a way to achieve sustainable transportation considering multicriteria evaluation of pavement strategies and sustainable solutions. This article presents a pavement strategy selection based on a new extension of fuzzy MCDM methods. The methodology is developed in two phases. First, the evaluation decision matrix is formulated on the basis of intersecting the “evaluation criteria” and “pavement strategies list.” Second, the proposed MCDM methods are developed: multilayer dual hesitant fuzzy weighted zero inconsistency (DH-FWZIC) to assign weights to the pavement's evaluation criteria followed by dual hesitant fuzzy decision by opinion score method (DH-FDOSM) for selecting the best pavement strategy. Four alternatives, namely, flexible/asphalt, rigid/concrete, reflective, and permeable alternatives pavement strategies, are evaluated on the basis of 30 criteria. Results show the following. 1) The multilayer DH-FWZIC method has weighted the pavement strategies’ evaluation criteria at three layers in a consistent manner, showing that a region's environment criterion has the highest final weight (0.1215) and the lowest importance value (0.0089) assigned for its windy criterion. 2) According to DH-FDOSM, the flexible/asphalt pavement achieved the first rank among the four pavement strategies. Finally, the robustness of the developed framework was assessed by conducting sensitivity analysis and comparison study.
TL;DR: In this paper , a two-stage process of obtaining DMs' opinions based on opinion dynamics is developed to analyze the interaction of DMs before clustering, and a new method for determining the heterogeneous weights of the DMs and subgroups is discussed.
Abstract: Compared with group decision making (GDM), it is more difficult to reach a consensus in large-scale group decision making (LSGDM) owing to the large number of decision makers (DMs). Moreover, studies on LSGDM under social trust networks are obviously fewer than those on GDM, and rarely utilize opinion dynamics to analyze the interaction of DMs. Therefore, in the context of multi-criteria large-scale group decision making (MCLSGDM), an MCLSGDM consensus decision framework and a bounded confidence-based consensus optimization model are proposed. First, a trust propagation method considering the relative importance of the trust degrees (TDs) is proposed. Then, a two-stage process of obtaining DMs’ opinions based on opinion dynamics is developed to analyze the interaction of DMs before clustering. Furthermore, a new method for determining the heterogeneous weights of DMs and subgroups is discussed. Finally, to consider the adjustment willingness of DMs, this study proposes a two-stage optimization consensus model based on bounded confidence. In addition, a numerical example is used to further elaborate the above methods and models, and highlight their rationality and superiority through a series of simulation experiments and comparative analysis.