JournalTOCs

International Journal of Fuzzy Systems
Journal Prestige (SJR): 0.47

Citation Impact (citeScore): 2
Number of Followers: 0

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1562-2479 - ISSN (Online) 2199-3211
Published by Springer-Verlag

[2469 journals]

Geometric Ranking of Pythagorean Fuzzy Numbers Based on Upper Curved
Trapezoidal Area Characterization Score Function
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  Abstract: Abstract Pythagorean fuzzy number (PFN) is not only an extension of traditional intuitionistic fuzzy number (IFN), but also can deal with the decision-making problem of multi-attribute information in a wider range. In recent years, it has been rapidly developed and popularized in the field of decision science. Firstly, it is pointed out that there are some defects in the existing score function and ranking criteria of PFNs through counter examples, and the main causes of these defects are analyzed according to the geometric method. Secondly, IFNs are unified into the Pythagorean fuzzy environment, the new unified score function and geometric ranking criterion of IFNs and PFNs are proposed by the corresponding upper curved trapezoidal area and hesitation factor, and then the rationality of geometric ranking method and some basic properties of the score function are studied. Finally, the comparison of other ranking methods shows that the proposed method only needs a score function and its own hesitation to rank all PFNs uniformly, especially the accurate comparisons among equivalent PFNs are realized. This not only overcomes the contradiction and confusion caused by the respective ranking of traditional IFNs and PFNs, but also provides a theoretical basis for further expanding the fuzzy decision-making method of Pythagorean fuzzy sets.
  PubDate: 2022-08-09
Correction: New Extension of Fuzzy-Weighted Zero-Inconsistency and Fuzzy
Decision by Opinion Score Method Based on Cubic Pythagorean Fuzzy
Environment: A Benchmarking Case Study of Sign Language Recognition
Systems
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  PubDate: 2022-07-30
Fuzzy MADM-Based Model for Prioritization of Investment Risk in
Iran’s Mining Projects
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  Abstract: Abstract Investment in mineral industries always deals with various economic, political, technical, and environmental risks. Not paying enough attention to such risks increases the possibility of investment failure. Accordingly, in this study, the risk criteria for investing in mineral industries and sub-criteria for risk assessment according to the type of industries, including mineral exploration, exploitation, and processing projects, have been identified and prioritized. In this regard, a methodology is presented based on multi-attribute decision-making methods, including Fuzzy Delphi, DEMATEL, and ANP methods to identify and analyze the risk criteria and evaluate sub-criteria. The results indicate that financial and commercial, technical, HSE, infrastructural considerations, geological, political, security, and legal risks are the main risk measures affecting investment in mining projects with different priorities. From the viewpoint of investment risk assessment in mineral processing and exploration projects, technical, financial and commercial, and geological risk criteria are the most significant, respectively. Generally, all risk criteria must be considered to make a reasonable investment in the mining sector, especially those more important.
  PubDate: 2022-07-30
Adaptive Fuzzy Fault-Tolerant Control of Flexible Spacecraft with Rotating
Appendages
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  Abstract: Abstract This paper focuses on the attitude control problems of spacecraft with external interference and platform actuator failure. The Lagrange method is used to establish dynamic models of complex spacecraft composed of rotating appendages and platform, and the quaternion is used to describe spacecraft attitude kinematics. Second, a fault-tolerant control algorithm that combined adaptive fuzzy control with finite time sliding mode is proposed for the spacecraft platform, and fixed-time control schemes are proposed for rotating parts to achieve stable rotation of the spacecraft components relative to the platform. Finally, a numerical simulation is performed to verify the superiority and effectiveness of the proposed control laws, and comparisons with other control methods are presented.
  PubDate: 2022-07-26
A New Picture Fuzzy Entropy and Its Application Based on Combined Picture
Fuzzy Methodology with Partial Weight Information
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  Abstract: Abstract Picture fuzzy set (PFS) is more comprehensive tool than intuitionistic fuzzy set (IFS) for modeling the uncertain decision-making problems. In this paper, a new picture fuzzy entropy measure is proposed and proved that the proposed measure satisfies the axiomatic definition of entropy measures for picture fuzzy sets. Besides this, the useful mathematical properties of the new entropy measure are also investigated. The justification of the proposed picture fuzzy measure is established by discussing its particular cases and compares it with the existing entropy measures. Then, for the case where criteria weights are partially known, we used an entropy-based method to produce objective weights. For the uncertain environment, TODIM (portuguese acronym for interactive multicriteria decision-making) and ELECTRE methods are useful for practical problems. Based on the advantages of PFSs,TODIM, and ELECTRE, we proposed an integrated picture fuzzy TODIM-ELECTRE to combine the prominent benefits of these theories. We present the TODIM-ELECTRE model for PFS environment and express the computing steps in brief of this new established model. Thereafter, the superiority of the new model is verified by a numerical example of supplier selection and through comparative study with other existing methods.
  PubDate: 2022-07-24
Negative Stiffness Control of Quasi-Zero Stiffness Air Suspension via
Data-Driven Approach with Adaptive Fuzzy Neural Network Method
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  Abstract: Abstract To reduce effects of vibration of commercial vehicles during driving, this paper proposes a new semi-active quasi-zero stiffness air suspension to improve vibration isolation and a strategy for negative stiffness control, which guarantees commercial vehicles owning excellent vibration isolation performance under different driving conditions. The strategy is mainly composed of data driven approach and adaptive fuzzy neural network method. Firstly, support vector machine (SVM) is adopted to identify road condition. The load, speed and air pressure signals collected by sensors and indirectly obtained suspension dynamic deflection data are imported into the SVM model trained by data to obtain-specific road conditions. Then, with the obtained road condition, the optimal air pressure of the pneumatic linear actuators is searched, which decides the negative stiffness of the system and is later the target pressure. Adaptive fuzzy neural network which trained by large amount of data is used in the air pressure seeking process to make sure that the target air pressure of any driving condition is suitable. Finally, active disturbance rejection controller (ADRC) is applied to realize tracking control of target air pressure. Parameters of ADRC are set to adapt to variable driving conditions. The results of Hardware-in-Loop (HiL) tests indicate that the negative stiffness control strategy can effectively improve multi-objective performance of commercial vehicles under different driving conditions.
  PubDate: 2022-07-23
Editorial Message: Fuzzy Machine Learning Algorithms with Applications
Arising in Physical Problems
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  PubDate: 2022-07-20
Observer-Based Hybrid-Triggered Control for Nonlinear Networked Control
Systems with Disturbances
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  Abstract: Abstract In this paper, the hybrid-triggered control (HTC) problem for nonlinear networked control systems with external disturbance is investigated by employing Takagi–Sugeno (T–S) fuzzy model. First of all, the observers are constructed to estimate system state and disturbance, respectively. With the help of disturbance estimation and attenuation (DEA) technique, the influence of external disturbance is attenuated effectively. Next, the HTC strategy is proposed to save the limited network resource while maintaining the desirable system performance. Then sufficient condition is proposed to ensure the exponential stability of the resultant closed-loop system, and the observer-based fuzzy controller is designed by solving an optimization problem. Finally, the effectiveness of the developed method is verified by a practical example.
  PubDate: 2022-07-20
Fuzzy Command Filter Backstepping Control for Incommensurate
Fractional-Order Systems via Composite Learning
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  Abstract: Abstract This paper investigates the command filter backstepping control of uncertain fractional-order generalized strict-feedback nonlinear systems with input nonlinearities and functional uncertainties based on a composite learning method. The main motivations are that the simplification of backstepping control by providing the fractional-order command filter to avoid the calculation of the derivatives of virtual controller functions, and parameters convergence can be achieved without the strict persistency of excitation condition via fractional-order composite learning laws. In the controller design, both tracking errors and prediction errors are used to update adjusted parameters. Moreover, the analytic computation of derivatives of virtual inputs is not required. A set of lemmas is provided to analyze the affect of the fractional-order command filter, and the parameter convergence can be achieved without persistent excitation condition based on the composite learning technique. The control performance can be improved from the asymptotic stability to the M-L stability in closed-loop system. Finally, simulation result verifies the performance of the proposed method.
  PubDate: 2022-07-19
Personalization-Driven Consensus Reaching Model for Emergency Mission
Scheduling Schemes Selection in Large-Group Emergency Decision-Making with
Linguistic Distribution Preference Relationship
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  Abstract: Abstract Earth observation satellites are playing an increasingly important role in emergencies and have become an essential means of obtaining information. Given the suddenness and high urgency of emergencies, evaluating and selecting the emergency mission scheduling schemes in consideration evaluators’ individual preferences have become a high priority. Since the selection of emergency task scheduling scheme often involves many stakeholders and has the complex characteristics of a large group, how to obtain the common solution accepted by the majority of participants is the main challenge we face in the process of scheme selection. To cope with this challenge, this paper presents an original evaluation framework to address the selection problem of emergency mission scheduling schemes under a linguistic distribution preference relationship (LDPR) with numerous evaluators. In the proposed evaluation framework, a consistency-driven optimization model is proposed to produce interval numerical scales with personalized individual semantics by maximizing the consistency of the interval fuzzy preference relation transformed from the LDPR. Then, a personalization-driven consensus reaching model with acceptance probability is devised to model the individual willingness of evaluators to improve the satisfaction of consensus achievement. Following this, a collective method is designed to aggregate evaluators’ individual preferences based on the principle of justifiable granularity by maximizing the product of the specificity and coverage of information granule. Finally, a case study and some comparative analyses are undertaken to demonstrate the feasibility and validity of the proposed evaluation framework.
  PubDate: 2022-07-19
Learning Deception Using Fuzzy Multi-Level Reinforcement Learning in a
Multi-Defender One-Invader Differential Game
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  Abstract: Abstract Differential games are a class of game theory problems governed by differential equations. Differential games are often defined in the continuous domain and solved by the calculus of variations. However, modelling and solving these games are not straightforward tasks. Differential games, like game theory, are often involved with social dilemmas and social behaviours. Modelling these social phenomena with mathematical tools is often problematic. In this paper, we modelled deception to increase the pay-off in differential games. Deception is modelled as a bi-level policy system, and each level is modelled with a fuzzy controller. Fuzzy controllers are trained using a novel hierarchical fuzzy actor-critic learning algorithm. A deceitful player plays against multiple opponents. Although there is one ultimate goal for the player, it can choose multiple fake goals as well. The intention is to find a strategy to switch between the fake goals and the true goal to fool the opponents. The simulation platform is the game of guarding territories, a specific form of the pursuit–evasion games. We propose a method to easily increase the number of defenders with minimum changes in the policies. We create a universal structure that is not affected by the curse of dimensionality. We show that a discerning invader capable of using deception can improve its performance against the defenders by increasing the chance of invasion. We investigate the single-invader single-defender game and the single-invader multi-defender game. We study the superior invader and agents with the same speed. In all mentioned situations, the invader increases its pay-off by using deception versus being honest. A two-level policy system is used in this paper to model deception. The lower-level policy controls each goal’s invasion actions, while the higher-level policy controls deception where a successful game is not initially possible.
  PubDate: 2022-07-19
FLOWFS: Fast Learning-algorithm with Optimal Weights for Fuzzy Systems
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  Abstract: Abstract Although the Wang–Mendel (WM) method, a typical way of fuzzy modeling, can rapidly obtain fuzzy rules from data to construct fuzzy systems with good interpretability, its accuracy is not high. In the literatures, many optimization approaches are proposed to improve the accuracy of the WM method, but the optimization process is in general time-consuming. A shallow or single fuzzy system cannot deal with “the curse of dimensionality”, which makes it challenging to realize modeling of high-dimensional data. Inspired by deep neural networks, building deep fuzzy systems (DFS) through many shallow fuzzy systems will become a new direction of fuzzy modeling for high-dimensional data modeling. As a building block in DFS, each shallow fuzzy system should have the characteristics of high precision and fast training speed to ensure the operating efficiency of DFS. This paper proposes a Fast Learning-algorithm with Optimized Weights for Fuzzy Systems (FLOWFS) in the following three steps: (1) Obtain a basic fuzzy system with full rules through fast training; (2) Assign each fuzzy rule a weight starting with 1; (3) Develop a fast learning-algorithm optimal weights via the least square method and coefficient regularization. Aiming at the regression problems, FLOWFS is compared with the back propagation neural network (BP), radial basis function neural network (RBF), the WM method and long short-term memory (LSTM) on three classic datasets of UCI. The experimental results show that: (1) FLOWFS has achieved a higher prediction accuracy; (2) Compared with the WM method, FLOWFS not only has greater accuracy, but also is faster in training; (3) In terms of comprehensive indicators of accuracy and running-time, FLOWFS has the best performance. Therefore, FLOWFS can provide good fuzzy building blocks for DFS to enable fast modeling of high-dimensional data and achieve good interpretability and high accuracy.
  PubDate: 2022-07-19
Output Feedback Robust Fault-Tolerant Control of Interval Type-2 Fuzzy
Fractional Order Systems With Actuator Faults
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  Abstract: Abstract This paper is concerned with the stabilization problem for a class of uncertain nonlinear fractional order systems described by an interval type-2 fuzzy model, under actuator faults. To solve the problem, a robust fault-tolerant control (FTC) scheme composed mainly of an augmented non-fragile observer and a new-type \(H_\infty\) controller is developed. The resulting control system is with the following advantages. On the one hand, the system stability domain is extended significantly, attributed to introducing the concept of D-stability to the control design and stability analysis, instead of the conventional indirect Lyapunov theory. Theoretically, the stability domain can be expanded from the original half-plane to nearly the overall plane. On the other hand, the control system is robust against the measurement noise and external disturbances which however are not taken into account in the related works. This is achieved by adopting the \(H_\infty\) control method in a novel way, in which a new technical lemma is presented to solve real linear matrix inequalities (LMIs). In this way, the robust FTC design is also simplified, wherein the number of the required decision variables is reduced from four to two. Besides, the control design is less restrictive: three common requirements for the system matrix or the control gain selection are eliminated. Finally, the simulation results on an electrical circuit system and a numeral example both illustrate the above theoretical findings.
  PubDate: 2022-07-19
Research on Green Supplier Selection Based on Hesitant Fuzzy Set and
Extended LINMAP Method
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  Abstract: Abstract In the process of selecting green suppliers, there are hesitant information and preference differences, which affect the accuracy of decision-making results. The aim of this paper is to introduce a novel green supplier selection (GSS) approach considering the uncertain information and preference of decision makers (DMs). First, the relevant definitions of hesitant fuzzy set (HFS) are introduced. Then, the specific decision-making process of GSS based on the LINMAP method and HFS is given. In the decision process, the preference set is used to consider the preference degree of DMs to different suppliers, and the evaluation information of DMs is aggregated by HFWA operator. Further, the consistency and inconsistency of the decision-making are analyzed by calculating the TOPSIS indexes, and the optimal supplier is selected. Finally, the feasibility and validity of the proposed approach is illustrated by sensitivity analysis and case study.
  PubDate: 2022-07-14
Transparent but Accurate Evolutionary Regression Combining New Linguistic
Fuzzy Grammar and a Novel Interpretable Linear Extension
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  Abstract: Abstract Scientists must understand what machines do (systems should not behave like a black box), because in many cases how they predict is more important than what they predict. In this work, we propose a new extension of the fuzzy linguistic grammar and a mainly novel interpretable linear extension for regression problems, together with an enhanced new linguistic tree-based evolutionary multiobjective learning approach. This allows the general behavior of the data covered, as well as their specific variability, to be expressed as a single rule. In order to ensure the highest transparency and accuracy values, this learning process maximizes two widely accepted semantic metrics and also minimizes both the number of rules and the model mean squared error. The results obtained in 23 regression datasets show the effectiveness of the proposed method by applying statistical tests to the said metrics, which cover the different aspects of the interpretability of linguistic fuzzy models. This learning process has obtained the preservation of high-level semantics and less than 5 rules on average, while it still clearly outperforms some of the previous state-of-the-art linguistic fuzzy regression methods for learning interpretable regression linguistic fuzzy systems, and even to a competitive, pure accuracy-oriented linguistic learning approach. Finally, we analyze a case study in a real problem related to childhood obesity, and a real expert carries out the analysis shown.
  PubDate: 2022-07-12
Multigranulation Rough Set Methods and Applications Based on Neighborhood
Dominance Relation in Intuitionistic Fuzzy Datasets
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  Abstract: Abstract With the redundancy and complexity of information and data, how to acquire the samples that meet the requirements is an inevitable task in data analysis. There is a general consensus that the neighborhood rough set (NRS) has become the mainstream method for data mining and knowledge classification. Whereas, the limitations still exist in the neighborhood relation for it cannot more accurately reflect the dominance relations that commonly exist in actual data, nor can it select the required data according to different conditions. Enlightened by this idea, this paper focuses on the intuitionistic fuzzy neighborhood dominance relation, which both refines the relationship between samples in the neighborhood and mines the needed samples in data analysis. On this basis, we define the neighborhood dominance rough set (NDRS) model in intuitionistic fuzzy ordered information system (IFOIS). Moreover, we establish the multigranulation neighborhood dominance rough set (MNDRS) from multiple perspectives, and discuss related properties between NDRS and MNDRS. Meanwhile, we compare the NDRS with other rough set models from the roughness and the dependence degree viewpoints. Finally, we adopt nine UCI data sets and implement a series of experiments to illustrate the feasibility and effectiveness of the proposed models.
  PubDate: 2022-07-06
A Novel Hybrid Algorithm of Sea Object Classification Based on
Multi-sensor and Multi-level Track
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  Abstract: Abstract To classify sea targets of underwater and surface groups. A novel hybrid classification algorithm based on sonar, automatic identification system (AIS) and radar is proposed in this paper. The proposed method includes four parts. The data preprocessing, the multi-target data association, the multi-sensor multi-target correlation, and the underwater/surface probability distribution fusion. Firstly, the measurement data of multiple sensors are unified in time and space through space-time registration. Secondly, the measurement data of each sensor are separated into different target sets by Mahalanobis distance discriminant method. And each target is modeled by grey prediction GM (1,1) model subsequently, and the noise of data are filtered by Kalman filter (KF). Thirdly, it preliminarily determines the type of targets by Hungarian algorithm. Finally, the D–S evidence theory based on the Angle cosine and Lance distance (ALDS) is used to further determines the target type. The proposed methods can be applied when there is inconsistent evidence. Simulation results illustrate that the proposed algorithm is effective in decision support for sea target classification.
  PubDate: 2022-07-04
Event-Triggered Sliding Mode Control Using the Interval Type-2 Fuzzy Logic
for Steer-by-Wire Systems with Actuator Fault
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  Abstract: Abstract This paper proposes a fuzzy modeling and event-triggered adaptive sliding mode control for steer-by-wire (SbW) systems subject to uncertain nonlinearity, time-varying perturbation, actuator fault, and limited communication resources. First, an interval type-2 fuzzy logic system (IT2 FLS) based on Lyapunov's adaptive scheme is built to model the uncertain nonlinearity. Then, an event-triggered adaptive sliding mode control method is designed to overcome the limited communication resources, time-varying perturbation, and actuator fault. This method eliminates the chattering phenomenon by utilizing nested adaptive technology and has practical finite-time stability. Theoretical analysis shows that the Zeno phenomenon is excluded. Finally, the validity of the methods is evaluated using simulations and vehicle experiments.
  PubDate: 2022-07-04
Asynchronous Dynamic Output Feedback Control for Delayed Fuzzy Stochastic
Markov Jump Systems Based on HMM Strategy
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  Abstract: Abstract This paper addresses the asynchronous robust \(H_{\infty }\) dynamic output feedback control for Takagi–Sugeno fuzzy uncertain stochastic Markov jump systems with time-varying delays. The modes of devised fuzzy controller run asynchronously with the modes of controlled plant, which is described through a hidden Markov model. Dynamic output feedback control is that the output feedback only takes the output signal that the object can detect as the feedback signal. Because the measurement output is local information, the feedback is also local. Although it is feasible, it cannot reflect the overall situation. The fuzzy dynamic output feedback adopts parallel distribution compensation technique, which can realize global feedback, but realizing this feedback control is full of difficulties and challenges. And although introducing more variables can make the condition less conservative, it also increases the number of linear matrix inequalities and computational complexity. Improved conditions are acquired to ensure the robust exponential mean-square stability and \(H_{\infty }\) performance index for the closed-loop fuzzy uncertain stochastic Markov jump systems. Based on exponential mean-square stability, the asynchronous fuzzy dynamic output feedback controller is realized in terms of linear matrix inequalities. A numerical example and a single-link robot arm are employed to show the effectiveness and correctness of the method proposed in this paper.
  PubDate: 2022-07-01
Computer Application in Game Map Path-Finding Based on Fuzzy Logic Dynamic
Hierarchical Ant Colony Algorithm
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  Abstract: Abstract A large number of researchers recently focused on intelligent analysis about algorithms for path-finding concerning games. Currently, the most modern application in game map path-finding is based on the fuzzy logic (FL) algorithm. It demonstrates that these strategies increase the quest performance and have a simpler direction to enhance the algorithm’s game. Hence, in this paper, a Fuzzy logic-based game path-finding Framework (FLGPFF) has been proposed to reduce the search space to improve search speed and path planning on uneven surfaces. The suggested FLGPFF method uses computer vision to simulate the fuzzy dynamic game path’s algorithm using possible multi-purpose platforms. A computer vision explores the whole game map path and finds a way between two remote places. The fuzzy method uses a fuzzier, center average demulsifier, and product inference engine. The ant colony algorithm is used for the complex environment with many moving obstacles and, using a weighted artificial field method, it calculates a trajectory from the target’s initial location. Thus, the experimental results show the FLGPFF to enhance path allocation prediction and less delay time than other popular methods. The simulation outcome recommended that FLGPFF can improve the accuracy ratio (95.2%), search timer per map (96.3%), game quality improvement ratio (98.5%), performance ratio (97.8%), and comparison of the path-finding model (95.1%).
  PubDate: 2022-07-01