International Journal of Fuzzy Systems最新文献

In the paper, a dynamic surface-based adaptive fuzzy fixed-time fault-tolerant control scheme is developed for nonstrict feedback nonlinear systems with non-affine faults. Firstly, the computational complexity is reduced by adopting dynamic surface control technique, and unknown nonlinear functions are approximated with the help of fuzzy logic systems. Secondly, non-affine faults involving system states and controller output are taken into account and treated by transforming it into nonlinear in the unknown parameters. Then, under the framework of fixed-time stability, a novel adaptive fuzzy fault-tolerant control strategy is designed so that the closed-loop system is semi-globally practically fixed-time stable. Finally, a numerical simulation and a model simulation are given to demonstrate the effectiveness of the proposed control scheme.

Improvements are necessary for the performance improvements of the digital twin technology developed for the virtual energy market on the Metaverse platform. However, more important factors need to be improved first to avoid excessive increases in costs. Thus, a priority analysis needs to be carried out to determine the variables that most affect the performance of technology investments. Accordingly, the purpose of this study is to evaluate the investments of digital twin technologies for virtual energy market in the Metaverse. A novel artificial intelligence-based fuzzy decision-making model is constructed to reach this objective. Firstly, the expert choices are prioritized with artificial intelligence-based decision-making method. Secondly, the investment priorities are analyzed for digital twin technologies with quantum picture fuzzy rough sets (QPFRS)-based Multi Stepwise Weight Assessment Ratio Analysis (M-SWARA). Finally, the alternatives for virtual energy market in the metaverse are ranked by VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje). There are limited studies in the literature that computes the weights of the experts while generating a decision-making model. Therefore, the main contribution of this study is integrating the artificial intelligence approach and fuzzy multi-criteria decision-making methodology. Within this scope, an artificial intelligence-based application is performed when creating the decision matrix. Owing to this issue, the importance weights of experts are determined according to the qualifications of these people. This situation contributes to the results obtained being more realistic. The findings demonstrate that operational performance is the most important indicator for the improvements of the digital twin technology investments for virtual energy markets in metaverse platform because it has the greatest weight (0.267). Furthermore, integrated data production is another critical factor for the performance increase of these projects with the weight of 0.257. It is also concluded that optimization of energy consumption with smart grids has the best ranking performance among the alternatives.

This paper is part of the increasing interest regarding the application of interval type-3 fuzzy logic in real-world problems, where a better handling of uncertainty can be useful in achieving enhanced results. The main contribution of this paper is the proposal of new methods, such as Interval Type-3 Reduction and a practical way for modeling Interval Type-3 Membership Functions, based on the Footprint of Uncertainty (FOU) and Core of Uncertainty (COU) concepts, which reduce the gap between the theory and the practical implementation of Mamdani Interval Type-3 Fuzzy Systems. The main aim of the paper is not proving the superiority of Interval Type-3 Fuzzy Systems but providing a framework and a comprehensive illustration of the theory concepts to help future research work in developing optimization methodologies and new applications for this kind of systems, as well as finding their potential applicability, which can result from their ability in handling more complex uncertainty. Simulation results with two illustrative application examples show the potential of the presented approach in achieving an efficient implementation of Interval type-3 fuzzy systems.

This paper studies the mixed (H_2/H_infty) control for Takagi–Sugeno (T–S) fuzzy Markovian jump systems (MJSs) subject to random delays and multiple uncertain transition probabilities. In contrast to existing research, this study presents uncertainty parameters, external disturbance, random delays, and uncertain transition probabilities simultaneously in a unified T–S fuzzy model. Specifically, this study examines multiple Markov chains with partially unknown transition probabilities. These complex imperfections have a substantial adverse impact on system performance and the associated challenge of mixed (H_2/H_infty) control remains unresolved. Our innovative contributions are described as follows. The proposed approach utilizes free-weighting matrix technique and Lyapunov–Krasovskii functional to get the (H_2/H_infty) controller, which ensures that the stochastic T–S fuzzy systems exhibit stochastic stability and comply with the (H_infty) performance index.

Aggregation operators are used for aggregating the collection of finite information into a singleton set. The Aczel-Alsina t-norm and t-conorm are very useful for constructing any kind of new aggregation operators, which was presented by Aczel and Alsina in 1982. Moreover, complex Pythagorean fuzzy (CPF) sets and hesitant fuzzy (HF) sets are the most generalized and very useful techniques to cope with unreliable and awkward information in genuine life problems. In this manuscript, we combine the HF set and CPF set to derive the complex Pythagorean hesitant fuzzy (CPHF) set and its fundamental laws. Furthermore, we evaluate the Aczel-Alsina operational laws based on Aczel-Alsina norms and CPHF information. Additionally, based on the Aczel-Alsina operational laws for CPHF information, we investigate the CPHF Aczel-Alsina-weighted averaging (CPHFAAWA) operator, CPHF Aczel-Alsina-ordered weighted averaging (CPHFAAOWA) operator, CPHF Aczel-Alsina-weighted geometric (CPHFAAWG) operator, and CPHF Aczel-Alsina-ordered weighted geometric (CPHFAAOWG) operator. Some remarkable properties are also examined for the invented theory. Moreover, a multi-attribute decision-making (MADM) technique is presented based on discovered operators for CPHF information. Finally, we aim to illustrate some examples for comparing the proposed techniques with some existing ones to show the worth and feasibility of the discovered approaches.

Vehicle hydraulic braking systems are widely used, structurally complex. Reliability allocation during the design phase is crucial, yet there is a relatively limited body of research on this subject. In response, a new method for vehicle hydraulic braking system is proposed: the comprehensive reliability allocation method based on partial fuzzy ratings and considering failure correlation (CRA-PFRAFC). Based on the analysis of reliability allocation criteria impacting the braking system, the fuzzy set theory is introduced into the comprehensive allocation method, and the criteria with strong subjective dependence are fuzzy evaluated. The braking system allocation model is established by Gumbel Copula function. According to the set reliability target, the model is solved to allocate the failure rates to each subsystem according to the allocation vector. An example illustrates the advantages of this method. The results show that the reliability of the brake assembly is the lowest, while the reliability of the vacuum booster system is the highest. By fuzzy rating the failure severity and failure occurrence, the subjective quantification problem in traditional method is avoided. Meanwhile, compared with the traditional subsystem independent assumption model, this method is more realistic, and the failure rate of subsystem allocation is increased by 20% on average. Therefore, this study provides necessary and effective theoretical basis for reducing the design and manufacturing costs of vehicle hydraulic braking systems.

In this paper, the issue of exponential stabilization and sampled-data controller design for Takagi–Sugeno fuzzy large-scale networked control systems is studied by using the reduction-based ordinary differential equation prediction method. For the problem that matrices cannot be multiplied directly during the process of designing the sampled-data controller in this paper, a matrix dimensional transformation method is proposed. Firstly, a type of two-sided mode-dependent loop-based Lyapunov–Krasovskii functional is constructed, which compensates for the large delay and makes fuller use of the information in sampled-data interval. Secondly, the proposed method is used to give the design scheme of an aperiodic sampled-data controller, and furthermore, an iterative algorithm to verify the effectiveness of the requested control gains is provided. Finally, two coupled vehicle pendulum systems and two-area interconnected power systems are applied to demonstrate the efficiency of the presented approach.

Electroencephalogram (EEG)-based emotion recognition plays an important role in brain-computer interface and mental health monitoring. The large amount of EEG data but the lacks of labeling, multi-feature attribute, and data uncertainty are the difficulties in its recognition problem. A multi-view semi-supervised Takagi–Sugeno–Kang (MV-SS-TSK) fuzzy system is developed for EEG emotion classification in this paper. In the learning of fuzzy system consequent, firstly, a novel joint learning of semi-supervised learning, sparse representation, and low-rank coding is developed for semi-supervised sparse consequent factor learning, which makes the consequent parameter learning as a pseudo-label-only optimization problem. In particular, to simplify fuzzy rules, the sparse constraint term ensures the consequent parameters to be sparse in rows. Secondly, the consequent factor learning in a single feature view is extended into the multi-view learning model. In particular, low-rank coding is considered in multi-view semi-supervised consequent parameter learning. The low-rank constraint on view-shared component of consequent factor is implemented to exploit global data structure. The sparse constraint on view-dependent component of consequent factor is implemented to retain the feature diversity representation. By minimizing the intersection between view-shared component and view-specific components for different views, MV-SS-TSK can take advantage of the intrinsic relationship between various features and capture the consistency from multi-view features. Experiments on the SEED dataset show the superior performance of the proposed fuzzy system.

This paper examines the event-triggered filtering problem related to discrete-time nonlinear systems that are described by interval type-2 (IT2) fuzzy models. The filter being studied is prone to a non-stationary Markovian process when both quantization output and deception attack are taken into account simultaneously. It is proposed to implement an asynchronous IT2 fuzzy filter characterized by two different piecewise-stationary Markov chains specifying the deception attacks and the modes of the system. A new event-triggering protocol (ETP) is investigated as a means of reducing unnecessary signal transmissions on the communication channel. Based on the linear matrix inequality analysis and using the information on upper and lower membership functions, it is demonstrated that stochastic sufficient conditions exist for the desired filter such that it exhibits mean square stability and achieves the prescribed mixed (H_infty ) and passivity performance index. Moreover, an optimization-based problem for computing filter gains is proposed. An experimental numerical illustration based on a truck-trailer system is used to validate the developed scheme.

In large-scale group decision-making (LSGDM) for dry hot rock exploration location selection, decision-makers are limited by their professional fields and knowledge background, and it is difficult to provide complete evaluation information. However, brainstorming is the main advantage of LSGDM. To maximize the professional contributions of various decision-makers, a multi-attribute LSGDM method based on three-way decision (TWD) and intuitionistic fuzzy concept-oriented (IFC) is proposed. Firstly, according to the characteristics of IFC, a description of the LSGDM problem based on IFC is given; then, an LSGDM model based on TWD is proposed to classify and rank alternatives. Two algorithmic descriptions are given, namely consensus reaching process algorithm and algorithm for classifying and ranking alternatives. Then, taking dry hot rock exploration location selection as an example, the execution steps of this model were elaborated in detail, and the final classification and ranking results were obtained. Finally, the effectiveness and feasibility of this model were analyzed based on experimental results, and the influence of various parameters on the results was also studied.