Category: IEEE Transactions on Fuzzy Systems

At present, there is no unified method for solving multiattribute decision-making problems. In this paper, we propose two methods that benefit from some novel fuzzy rough set models. Some theoretical preliminaries pave the way. First, by means of a fuzzy logical implicator Iand a triangular norm T, four types of coverings-based variable precision (I, T)-fuzzy rough set models are proposed. They can be used to deal with misclassification and perturbation (here, misclassification refers to error or missing values in classification, while perturbation refers to small changes in digital data). Second, the properties and the relationships among these models are investigated. Finally, we rely on their remarkable features in order to establish two approaches to multiattribute decision-making. Some numerical examples illustrate the application of these new approaches. The sensitivity and comparative analyses show that the respective ranking results produced by these decision-making methods have a high consensus for multiattribute decision-making problems with fuzzy evaluation information.

In this paper, the problem of event-triggered dynamic output feedback model predictive control (OFMPC) for nonlinear networked control systems (NCSs) with packet loss and bounded disturbance is studied. Interval type-2 (IT2) Takagi-Sugeno fuzzy model is exploited to represent the nonlinear plant with parameter uncertainties, which can be captured by the lower and upper membership functions. Whether or not the measured output should be released into unreliable network links is determined by the error between the current measured output and the latest event-triggered output. The Bernoulli random binary distribution is used to describe the process of packet loss in NCSs. This paper proposes the following: 1) the synthesis approach, including the design of the parameter-dependent dynamic output feedback controller by solving an online MPC optimization problem, which minimizes the upper bound of an infinite time horizon quadratic objective function respecting input and state constraints; 2) the guarantee of recursive feasibility and quadratical stability of the closed-loop system by applying the quadratic boundedness technique. Moreover, an algorithm of tightening the ellipsoidal bounds of state error is added to improve the control performance. The simulation and comparison studies are performed to demonstrate the usefulness and availability of the presented new techniques.

Multiview and multiexemplar fuzzy clustering aims at effectively integrating the fuzzy membership matrix of each individual view to search for a final partition of objects in which each cluster may well be represented by one and even multiple exemplars. However, how to integrate the corresponding fuzzy membership matrix of each view such that enhanced clustering performance can be theoretically guaranteed still keeps an open topic. In this study, with the proposed exemplar invariant assumption that an exemplar of a cluster in one view is always an exemplar of that cluster in each other view, we demonstrate that multiview & multiexemplar fuzzy clustering has a theoretical guarantee of enhanced clustering performance. Based on the above-mentioned theoretical result, we develop a novel multiview & multiexemplar fuzzy clustering approach (M²FC). The key features of the proposed approach are: first, embed a quadratic penalization term into its objective function to minimize the discrepancy of exemplars across different views such that the exemplar invariant assumption can be met as much as possible; and second, optimize the proposed objective function of the proposed approach by applying the Lagrangian multiplier method and Karush-Kuhn-Tuchker conditions to assure nonnegative fuzzy memberships. Extensive experimental results show that M²FC outperforms the existing state-of-the-art multiview approaches in most cases.

Accelerated degradation testing (ADT) aids the reliability and lifetime evaluations for highly reliable products. In engineering applications, the number of test items is generally small due to finance or testing resource constraints, which leads to the rare knowledge to evaluate reliability and lifetime. Consequently, the epistemic uncertainty is embedded in ADT data and the large-sample based probability theory is no longer appropriate. In this paper, we introduce the uncertainty theory, which is a theory different from the probability theory, to account for such uncertainty due to small samples and build up a framework of ADT modeling. In this framework, an uncertain accelerated degradation model is first proposed based on the arithmetic Liu process. Then, the uncertain statistics for parameter estimations are presented correspondingly, which is completely constructed on objectively observed ADT data. An application case and a simulation case are used to illustrate the proposed methodology. With further comparisons to the Wiener process based accelerated degradation model (WADM) and the Bayesian-WADM, the sensitivities of these models to sample sizes are explored and the results show that the proposed model is superior to the other two probability-based models under the small sample size.