Uncertain Fuzzy Research Articles

Game-Based Collaborative Scheduling With Fuzzy Uncertain Migration in Cloud Manufacturing

Game-Based Collaborative Scheduling With Fuzzy Uncertain Migration in Cloud Manufacturing

Application of Interval Valued Intuitionistic Fuzzy Uncertain MCDM Methodology for Ph.D Supervisor Selection Problem

The selection of Ph.D (Doctor of Philosophy) supervisor is always a vital and interesting problem in academia and especially for students who want to carry out Ph.D. Nowadays, selecting a supervisor for Ph.D in a scientific manner becomes a challenge for any student because of the variety of options available to the scholar. In this context, the present study aims to formulate a model for Ph.D. supervisor selection from the offered alternatives in an academic institute. A hybrid multi-criteria decision making (MCDM) framework has been applied to select the suitable supervisor of the student’s preferred criteria under interval-valued intuitionistic fuzzy (IVIF) scenario. The IVIF Analytic Hierarchy Process (AHP) has been employed to prioritize the criteria, whereas IVIF Technique for order preference by similarity to ideal solution (TOPSIS) technique is engaged to rank the available supervisors based on criteria weight. A set of eight criteria and five alternatives have been considered for modeling the problem. Moreover, the potential criteria are weighted and ranked by the multiple decision makers in the present study. To examine the consistency and robustness of the proposed integrated approach, sensitivity analysis and comparative analysis have been carried out. From all the analyses, it can be conferred that the suggested approach is quite useful to apply in different decision-making scenarios.

Conditional Sliding Mode Control-Based Fixed-Time Stabilization of Fuzzy Uncertain Complex System

Conditional Sliding Mode Control-Based Fixed-Time Stabilization of Fuzzy Uncertain Complex System

Guaranteed Set-Membership Estimation for Local Nonlinear Uncertain Fuzzy Systems Subject to Partially Decouplable Unknown Inputs

Local nonlinear fuzzy systems are useful for control solutions due to their ability to handle unmeasurable/inexact premise variables and reduce computational complexity. However, their estimation problems still require further development. This paper addresses this issue by investigating set-membership estimation for a class of discrete-time local nonlinear uncertain Takagi–Sugeno fuzzy systems with guaranteed performance. We propose a new observer architecture that solves partially decouplable unknown inputs and converts nonlinear error dynamics into a linear parameter-varying type. Using the systematic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _\infty$</tex-math></inline-formula> -technique, the design conditions in the form of linear matrix inequalities ensure stability and output performance of the state estimation. We also derive a straightforward and effective zonotopic analysis method, considering the fuzzy and local nonlinear context, for less conservative results without using any specific interval set computation. Furthermore, a fast fault detection logic is proposed as an application of the set-membership estimation. Finally, we demonstrate the feasibility and advantages of our approach through three compelling examples, showcasing its efficacy in different scenarios.

A probability-based fuzzy algorithm for multi-attribute decision-analysis with application to aviation disaster decision-making

This research addresses the challenges of multi-attribute group decision-making in a hesitant fuzzy environment, where randomness and uncertainty introduce biases into practical making choices. A probability-based double uncertain fuzzy (PDHF) algorithm is proposed to overcome these challenges. The algorithm constructs probability-based double hesitant fuzzy information matrices for each decision expert. The maximum deviation and entropy methods determine objective weights for decision experts and attribute indicators. Combining an improved scoring function and deviation function generates a comprehensive decision evaluation information matrix for decision experts. Subsequently, a decision algorithm that integrates probability-based double hesitant fuzzy sets with the preference ranking organization method (PROM) is employed to obtain the final decision result. As part of a case study, the algorithm is used to evaluate emergency action plans for aviation disaster accidents. Comparing the results with those from the TOPSIS, VIKOR, and PDHFS decision-making algorithms shows that the suggested PDHF algorithm works well and can be trusted for the multi-attribute group decision-making problems in hesitant fuzzy environments.

Interval-Valued Picture Fuzzy Uncertain Linguistic Dombi Operators and Their Application in Industrial Fund Selection

Interval-Valued Picture Fuzzy Uncertain Linguistic Dombi Operators and Their Application in Industrial Fund Selection

Observer-Based Fault Diagnosis and Fault-Tolerant Tracking Control for T-S Fuzzy Uncertain System Affected by Simultaneous Sensor and Actuator Faults

Observer-Based Fault Diagnosis and Fault-Tolerant Tracking Control for T-S Fuzzy Uncertain System Affected by Simultaneous Sensor and Actuator Faults

Bipartite Consensus for Takagi-Sugeno Fuzzy Uncertain Multi-Agent Systems With Gain Fluctuations

This paper examines the problem of bipartite consensus for Takagi-Sugeno fuzzy multi-agent systems subject to uncertainties. The principal intention of this work is to develop a non-fragile controller through which the considered multi-agent system can achieve bipartite consensus. An undirected signed graph is considered to describe the cooperative and competitive interaction among neighboring agents. On this circumstance, we propose a consensus protocol by the utilization of graph theory. Further, with the aid of Lyapunov stability theory, the sufficient conditions are derived in the form of linear matrix inequalities which ensures the bipartite consensus of the considered multi-agent system. At last, two numerical examples are provided with their simulations to describe the viability of the developed theoretical findings.

A study on the Heronian mean operators for managing complex picture fuzzy uncertain linguistic settings and their application in decision making

The major influence of this manuscript is to diagnose a valuable and considerable technique of Complex Picture Fuzzy Uncertain Linguistic setting and described its useful and valuable operational laws. The theory of Complex Picture Fuzzy Uncertain Linguistic information is massive modified and generalized than the Complex intuitionistic Fuzzy Uncertain Linguistic, Complex Fuzzy Uncertain Linguistic, Fuzzy Uncertain Linguistic and Uncertain Linguistic information. Keeping the supremacy and dominancy of the Complex Picture Fuzzy Uncertain Linguistic information, we investigated the Complex Picture Fuzzy Uncertain Linguistic Arithmetic Heronian Mean, Complex Picture Fuzzy Uncertain Linguistic Weighted Arithmetic Heronian Mean, Complex Picture Fuzzy Uncertain Linguistic Geometric Heronian Mean and Complex Picture Fuzzy Uncertain Linguistic Weighted Geometric Heronian Mean operators. The property of idempotency, boundedness, monotonicity, and various well-known results with certain specific cases of the invented work are also deliberated. Furthermore, in the availability of the above-proposed analysis, we constructed a multi-attribute decision-making technique by considering the diagnosed operators for complex picture fuzzy uncertain linguistic information to enhance the worth and rationality of the invented theory. Finally, we illustrated the merits and restrictions of the novel operators by comparing them with certain prevailing operators based on fuzzy generalization. Finally, in the presence of evaluated examples, we compared the pioneered operators with various existing operators to enhance the feasibility and worth of the invented operators.

Uncertainty Propagation Analysis of Fuzzy Uncertain Structures Involving Imprecise Membership Functions

Uncertain structures may exhibit fuzzy uncertainty involving imprecise membership function (FuIMF). In this study, the uncertain parameters in FuIMF case are characterized as fuzzy variables, whereas the key parameters of their membership functions are treated as interval variables rather than exact values. Two ideas are put forward to handle FuIMF variables. First, the interval-boundary interval method (IBIM) is derived to conduct uncertainty propagation analysis, in which the [Formula: see text]-cut of FuIMF variables are considered as interval-boundary intervals. Second, the [Formula: see text]-cut of FuIMF variables are presented by the conservative and radical approximations, and the conservative and radical approximations method I (CRAM I) is proposed to conduct uncertainty propagation analysis. To further promote the computational efficiency, the conservative and radical approximations method II (CRAM II) is developed. Afterwards, a reference method based on Monte Carlo simulation is presented to verify the proposed methods. Finally, the effectiveness of proposed methods is demonstrated by numerical examples.

Evaluation of the Environmental Effect of Automated Vehicles Based on IVIULWG Operator Development

The automated vehicle (AV) industry is a new growing industry of great interest. The environmental friendliness of AVs represents a core characteristic of their leap-forward development. The environmental effect of AVs, including its evaluation framework and algorithm, is a leading research area for continued technological innovation and market development in this field. This study focuses on three environmental aspects: the energy effect of AVs, the traffic effect of AVs on ground space, and the air effect of AVs on three-dimensional atmospheric space. First, an environmental effect evaluation indicator system that includes nine indices for AVs is constructed to be the basis for management decision making. Second, the interval-valued intuitionistic uncertain fuzzy (IVIUF) evaluation variable is used to solve the problem of data features that incorporate “crisp numbers” and “fuzzy numbers”. Moreover, geometric weights are added based on the generalized correlation aggregation operator. Then, the IVIUL-weighted geometric (IVIULWG) operator is developed, which includes the objective and subjective information of “crisp numbers” and “fuzzy numbers” and makes the mathematical characteristics more scientific and accurate. Finally, an evaluation example is used to validate the effectiveness and practicability of the algorithm.

Robust Nonfragile Guaranteed Cost Control for Uncertain Fuzzy Markov Jump Systems with Time-Varying Delays

This paper aims to investigate the problem of robust nonfragile guaranteed cost control for uncertain discrete-time Takagi–Sugeno fuzzy systems with Markov jumping parameters and time-varying delay. A nonfragile fuzzy-basis-dependent and mode-dependent controller is designed and a sufficient condition is developed to ensure that the resulting closed-loop system is robust asymptotically stable in mean square with guaranteed cost index not exceeding the specified upper bound. Subsequently, the controller gain and minimum upper bound of the guaranteed cost index can be obtained by solving a set of linear matrix inequalities. Compared with the existing literature, this paper enormously reduces the conservatism of the result obtained. Finally, numerical and practical examples are provided to demonstrate the performance of the proposed approach.

Nonfragile Sampled-Data Filtering of Uncertain Fuzzy Systems With Time-Varying Delays

This article studies the nonfragile sampled-data filtering problems for Takagi-Sugeno fuzzy systems with uncertainties and delays. By introducing two delay-product-type terms and other augmented terms, a novel Lyapunov-Krasovskii functional containing more detailed information of time delays is constructed. With the help of some new bounding inequalities, improved stability results for the addressed systems are established. Besides, a resilient sampled-data controller is devised with less cost. Furthermore, the efficiency of the obtained criteria is illustrated by a numerical example.

An Approach of Interval-Valued Picture Fuzzy Uncertain Linguistic Aggregation Operator and Their Application on Supplier Selection Decision-Making in Logistics Service Value Concretion

With respect to multiple criteria group decision-making (MCGDM) problems in which both the criteria weights and the expert weights take the form of crisp numbers and attribute values take the form of interval-valued picture fuzzy uncertain linguistic numbers, some new group decision-making analysis methods are developed. Firstly, some operational laws, expected value, and accuracy function of interval-valued picture fuzzy uncertain linguistic numbers are introduced. Then, an interval-valued picture fuzzy uncertain linguistic averaging and geometric aggregation operators are developed. Furthermore, some desirable properties of the developed operators, such as commutativity, idempotency, and monotonicity, have been studied. Based on these operators, an approach to multiple criteria group decision-making with interval-valued picture fuzzy uncertain linguistic information has been proposed. Finally, a practical example of 3PL supplier selection in logistics service value concretion is taken to test the defined method and to expose the effectiveness of the defined model.

Stochastic Switched Sampled-Data Control for Uncertain Fuzzy Systems with Packet Dropout

This paper addresses the stability and stabilization problems of uncertain Takagi–Sugeno (T-S) fuzzy system with control packet dropout. The process of packet loss is proposed, which is modeled according to some white noise sequences of Bernoulli distribution. Then, under the zero-input strategy, a newly stochastic switched sampled-data controller is introduced. Based on the Lyapunov function method, a novel Lyapunov–Krasovskii (LKF) function is constructed via introducing a fuzzy membership functions (FMFs), which can use the information about the actual sampling pattern. Each term of the LKF need not be positive, but it needs to be positive at sampling instants. Using the reciprocally convex method and relaxed free-matrix-based (FMB) integral inequality, novel stabilization criteria are established to guarantee that the T-S fuzzy system is stochastic stable when the control packet dropout occurs in a random way. Based on the linear matrix inequalities (LMIs), a fuzzy controller design algorithm for sampled data is presented to obtain a larger sampling interval. Finally, two numerical examples are used to verify the effectiveness and advantages of the proposed method.

Thick Fuzzy Sets (TFSs) and Their Potential Use in Uncertain Fuzzy Computations and Modeling

This article aims at proposing the concept of thick fuzzy sets (TFSs). A TFS is based on the joint use of thick sets (TSs) and α-cuts concepts. A TFS is represented by a family of nested TSs. A TS is an uncertain set, which is represented by a pair of crisp sets (CSs). These CSs characterize the upper and lower bounds of the TS. Therefore, a TS can be regarded as an interval of CSs. In this framework, as a type-1 fuzzy set (T1FS) is regarded as a family of nested CSs, a TFS can be represented by a family of nested TSs. Furthermore, according to the vertical dimension α, a TFS can be regarded as an interval with T1FS bounds. The potentialities of the TFS concept have been validated using application examples where a real-world application for modeling the zone explored by an underwater robot is given.

Fault-Tolerant Control for Stochastic Switched IT2 Fuzzy Uncertain Time-Delayed Nonlinear Systems.

This article devotes to solve the fault-tolerant control problem based on interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy stochastic switched uncertain time-delayed systems with signal quantization. Stochastic switched systems can model a dynamic structure susceptible to abrupt faults, making it more practically significant in power systems or economic systems. The core design is an observer-based fault-tolerant control scheme that can estimate incomplete measurable variables and eliminate the influence of fault dynamically well and enhancing the robust stability of the systems subject to quantization effects. A novel method in seeking the upper bound solution of time-varying delay efficiently decreases conservativeness, especially for the proposed time-delayed system. The simulated analysis is specified to verify the availability and validity of the obtained design method.

Energy Efficient Task Scheduling for Real-Time Embedded Systems in a Fuzzy Uncertain Environment

During the designing phase of real-time embedded systems (RTESs), available information on task characteristics is either incomplete or imprecise. So, task timing constraints are mostly approximated estimations by designers. This indicates that there are underlying uncertainties in these timing constraints, which can be appropriately modeled using fuzzy numbers. Moreover, feasible scheduling of tasks and energy efficiency are two essential requirements for better utilization and durability of RTESs. Thus, justifiable performance of this kind of systems warrants energy savings amid timely production of the computational outputs, although these two issues are mutually contradictory. This article reports a novel formulation of the energy efficient real-time scheduling problem in a fuzzy uncertain environment and proposes a novel solution approach called “ε-constraint coupled energy efficient genetic algorithm (ε-EEGA).” The working of the proposed approach is demonstrated taking a real-life example. Also, a thorough comparative analysis is provided considering well-known existing approaches including multiobjective evolutionary algorithms. Results are compared using popular performance metrics, which suggests that the proposed ε-EEGA is efficient in giving better energy savings with faster computations than its existing counterparts. Standard statistical tests such as analysis of variance and Kruskal-Wallis are performed.

Approaches to multiple attribute group decision making based on triangular cubic linguistic uncertain fuzzy aggregation operators

In this paper, triangular cubic linguistic uncertain fuzzy averaging (geometric) operator, triangular cubic linguistic uncertain fuzzy weighted averaging operator, triangular cubic linguistic uncertain fuzzy weighted geometric operator, triangular cubic linguistic uncertain fuzzy ordered weighted averaging operator, triangular cubic linguistic uncertain fuzzy ordered weighted geometric operator, triangular cubic linguistic uncertain fuzzy hybrid averaging operator, and triangular cubic linguistic uncertain fuzzy hybrid geometric operator for triangular cubic linguistic uncertain fuzzy numbers have been introduced. Furthermore, by using these aggregation operators an approach to multiple attribute group decision making with triangular cubic linguistic uncertain fuzzy information has been developed. Finally, a numerical example is constructed to validate the established approach.

Observer-Based Sliding Mode Control for Uncertain Fuzzy Systems via Event-Triggered Strategy

This paper investigates the problem of sliding mode observer design for a class of uncertain fuzzy time-delay systems based on an event-triggered strategy. The objective is to design an event-triggered mechanism by utilizing the information of system output and observer function. Based on the delay partitioning method and the Lyapunov–Krasovskii function approach, delay-dependent sufficient conditions are proposed to guarantee the overall system, including sliding mode dynamics and error dynamics, to be asymptotically stable with an $\mathcal {H}_{\infty }$ performance. Furthermore, an event-triggered sliding mode controller is synthesized to ensure that the system dynamics can be driven to the sliding region near the equilibrium point in finite time. Finally, a verification example is provided to demonstrate the feasibility and efficiency of the theoretical results presented.