Search

We explore switching techniques between q-fractional fuzzy sets (qFr sets) and various other classes of fuzzy sets to establish connections and provide a comprehensive framework. In particular, we examine the relationships between qFr sets and interval-valued fuzzy sets (IVFS), type 2 fuzzy sets(T2FS), intuitionistic fuzzy sets(IFS), Pythagorean fuzzy sets(PFS), q-rung orthopair fuzzy sets (q-ROFS), and linear diophantine fuzzy sets(LDFS). By examining these interconnections, we aim to understand better qFr sets and their applications in a wide range of fuzzy systems. It is possible to convert qFr sets into other fuzzy set models using the derived switching techniques, facilitating the utilization of existing methods and algorithms. The versatility of qFr sets, combined with the bridging techniques presented, holds promise for addressing complex problems in decision-making, pattern recognition, and other applications where uncertainty and imprecision play significant roles. Through case studies and practical applications, we illustrate the effectiveness and usefulness of the proposed switching techniques, showcasing their potential impact on real-world scenarios. [ABSTRACT FROM AUTHOR]

Today, the new coronavirus (Covid-19) has become a major global epidemic. Every day, a large proportion of the world's population is infected with the Covid-19 virus, and a significant proportion of those infected dies as a result of this virus. Because of the virus's infectious nature, prompt diagnosis, treatment, and quarantine are considered critical. In this paper, an automated method for detecting Covid-19 from chest X-ray images based on deep learning networks is presented. For the proposed deep learning network, a combination of convolutional neural networks with type-2 fuzzy activation function is used to deal with noise and uncertainty. In this study, Generative Adversarial Networks (GANs) were also used for data augmentation. Furthermore, the proposed network is resistant to Gaussian noise up to 10 dB. The final accuracy for the classification of the first scenario (healthy and Covid-19) and the second scenario (healthy, Pneumonia and Covid-19) is about 99% and 95%, respectively. In addition, the results of the proposed method in terms of accuracy, precision, sensitivity, and specificity in comparison with recent research are promising. For example, the proposed method for classifying the first scenario has 100% and 99% sensitivity and specificity, respectively. In the field of medical application, the proposed method can be used as a physician's assistant during patient treatment.

This paper focuses on the representation of opposite concepts as paired fuzzy concepts, under a restricted setting for general paired structures. In particular, examining the notion of bipolarity and the semantics of bivariate scales, the proposed setting allows accomplishing a faithful knowledge representation by means of appropriate aggregation functions, based on the separate evaluation of paired opposite concepts. In consequence, the specific semantics of different fuzzy models, with respect to their particular purposes as extensions of fuzzy sets, can be formally recovered and separated in the framework of paired structures, pointing out that, despite their formal equivalence, distinct models actually provide a quite different representational performance. [ABSTRACT FROM AUTHOR]

In this study, a new α-representation of the inexact T2 fuzzy sets programming model (α-RITF2SP) is proposed for water resources planning under uncertainties. The developed model is a hybrid of inexact type-2 (T2) fuzzy sets (FSs) optimal programming based on α-cuts and α-planes (z-slices) algorithms, and both can be used in problems of water resource management without a loss of generality. Therefore, this novel method represents a complementary method to combine both of these algorithms to make inexact T2FSs methods easier and faster for users under uncertainties. This method provides not only a reduction in computations when computing fuzzy functions, but also a map of the primary variable of T2FSs in the secondary membership function. The α-RITF2SP is applied to a real case study of the operation of water resource systems in the southern Min River basin that involves three cities: Xiamen, Zhangzhou, and Quanzhou. The results of the water allocations and benefits have been analyzed. The symptoms of results indicate the higher profit with a higher wastewater treatment rate. It also represents that this model has enhanced abilities and it is relevant and practical as well as can serve as a guide for the development of this regional economy.

Since Lotfi A. Zadeh introduced the concept of fuzzy sets in 1965, many authors have devoted their efforts to the study of these new sets, both from a theoretical and applied point of view. Fuzzy sets were later extended in order to get more adequate and flexible models of inference processes, where uncertainty, imprecision or vagueness is present. Type 2 fuzzy sets comprise one of such extensions. In this paper, we introduce and study an extension of the fuzzy numbers (type 1), the type 2 generalized fuzzy numbers and type 2 fuzzy numbers. Moreover, we also define a partial order on these sets, which extends into these sets the usual order on real numbers, which undoubtedly becomes a new option to be taken into account in the existing total preorders for ranking interval type 2 fuzzy numbers, which are a subset of type 2 generalized fuzzy numbers. [ABSTRACT FROM AUTHOR]

KM/EKM type reduction algorithm is difficult to used for real-time control of interval type 2 fuzzy logic for its inefficiency. Although the operation time of IASC and EIASC algorithm based on switching point calculation formula and monotonic relation is lower than KM/EKM algorithm, but its initial value is from each side points and its efficiency also needs to be improved. This paper proposed a new interval type 2 fuzzy sets type reduction algorithm, which used switching point calculation formula and initial left and right switching point to improve efficiency. The simulation results of two simulation show the practical and adaptable of this method. [ABSTRACT FROM AUTHOR]

This paper presents a new modeling and control fuzzy-based framework validated with real-time experiments on human participants experiencing stress via mental arithmetic cognitive tasks identified through psychophysiological markers. The ultimate aim of the modeling/control framework is to prevent performance breakdown in human–computer interactive systems with a special focus on human performance. Two designed modeling/control experiments which consist of carrying-out arithmetic operations of varying difficulty levels were performed by ten participants (operators) in the study. With this new technique, modeling is achieved through a new adaptive, self-organizing, and interpretable modeling framework based on general Type-2 fuzzy sets. This framework is able to learn in real time through the implementation of a restructured performance learning algorithm that identifies important features in the data without the need for prior training. The information learnt by the model is later exploited via an energy model based controller that infers adequate control actions by changing the difficulty level of the arithmetic operations in the human–computer interaction system; these actions being based on the most current psychophysiological state of the subject under study. The real-time implementation of the proposed modeling and control configurations for the human–machine interaction under study shows superior performance as compared to other forms of modeling and control, with minimal intervention in terms of model retraining or parameter retuning to deal with uncertainties, disturbances, and inter/intrasubject parameter variability. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce a new notion of fuzzy convexity as a generalization of the other types of convexities, and give some connections between type 2 fuzzy convexity and generalized fuzzy convexity. We also discuss the relationship of type 2 fuzzy convex set with generalized strongly convex fuzzy set and semistrongly convex fuzzy set. [ABSTRACT FROM AUTHOR]

Since Lotfi A. Zadeh introduced the concept of fuzzy sets in 1965, many authors have devoted their efforts to the study of these new sets, both from a theoretical and applied point of view. Fuzzy sets were later extended in order to get more adequate and flexible models of inference processes, where uncertainty, imprecision or vagueness is present. Type 2 fuzzy sets comprise one of such extensions. In this paper, we introduce and study an extension of the fuzzy numbers (type 1), the type 2 generalized fuzzy numbers and type 2 fuzzy numbers. Moreover, we also define a partial order on these sets, which extends into these sets the usual order on real numbers, which undoubtedly becomes a new option to be taken into account in the existing total preorders for ranking interval type 2 fuzzy numbers, which are a subset of type 2 generalized fuzzy numbers.

Abstract: In recognition of the idea that land cover classes are vague phenomena, a number of researchers have advocated modelling of Land Cover as fuzzy sets, as opposed to Boolean sets. Once vagueness is admitted, it is not sufficient to stop at modelling land cover simply as a fuzzy set, because any threshold map (α-cut) of the fuzzy set is itself a Boolean set. Rather it may be appropriate to model it as a type 2 fuzzy set recognising the existence of higher order vagueness. Using the example of estimating land cover extents in an area of the forest–savanna ecotone in Bolivia, this paper presents the results and possible advantages of a type 2 fuzzy analysis of land cover. The consideration of type 2 fuzzy sets reveals the depth of the uncertainty, and allows the analyst to be specific about minimum, maximum, and average extents of land cover types and even to report fuzzy area itself as a fuzzy number and to justify descriptive qualifications of the results. The nuanced analysis, which is possible with this approach, seems an advance for the scientific community concerned with the conceptualisation of land cover classes, but it may be a nuisance for those involved in policy matters. [Copyright &y& Elsevier]

This paper presents original concepts of control systems for an electrical drive with an elastic mechanical coupling between the motor and the driven mechanism. The synthesis procedure of the speed controller uses a proposed quality index (cost function) of system operation ensures the minimization of both tracking errors and torsional vibrations. Proper selection of the cost function focusses more on the reduction of torsional vibrations due to their negative influence on the drive's mechanical coupling vitality. The omission of the plant identification of an adaptive fuzzy controller was proposed. Two types of fuzzy controllers were analyzed, namely with type I and type II fuzzy membership functions. The novelty of the presented approach is in the application of a Petri transition layer in a type II fuzzy controller which reduces the numerical complexity in case of a large number of complicated type II fuzzy sets. The presented simulation and experimental results prove that the best dumping of mechanical vibrations ensures the adaptive fuzzy controller with type II functions and a Petri transition layer. [ABSTRACT FROM AUTHOR]

Fuzzy set theory has been suggested as a means for representing vague spatial phenomena, and is widely known for directly addressing some of the issues of vagueness such as the sorites paradox. Higher order vagueness is widely considered a necessary component of any theory of vagueness, but it is not so well known that it too is competently modelled by Type n Fuzzy sets. In this paper we explore the fuzzy representation of higher order vagueness with respect to spatial phenomena. Initially we relate the arguments on philosophical vagueness to Type n Fuzzy sets. As an example, we move on to an empirical generation of spatial Type 2 Fuzzy sets examining the spatial extent of mountain peaks in Scotland. We show that the Type 2 Fuzzy sets can be populated by using alternative parameterisations of a peak detection algorithm. Further ambiguities could also be explored using other parameters of this and other algorithms. We show some novel answers to interrogations of the mountain peaks of Scotland. The conclusion of this work is that higher order vagueness can be populated for Type 2 and higher fuzzy sets. It does not follow that it is always necessary to examine these higher order uncertainties, but a possible advantage in terms of the results of spatial inquiry is demonstrated. [ABSTRACT FROM AUTHOR]

In this study, a new α-representation of the inexact T2 fuzzy sets programming model (α-RITF2SP) is proposed for water resources planning under uncertainties. The developed model is a hybrid of inexact type-2 (T2) fuzzy sets (FSs) optimal programming based on α-cuts and α-planes (z-slices) algorithms, and both can be used in problems of water resource management without a loss of generality. Therefore, this novel method represents a complementary method to combine both of these algorithms to make inexact T2FSs methods easier and faster for users under uncertainties. This method provides not only a reduction in computations when computing fuzzy functions, but also a map of the primary variable of T2FSs in the secondary membership function. The α-RITF2SP is applied to a real case study of the operation of water resource systems in the southern Min River basin that involves three cities: Xiamen, Zhangzhou, and Quanzhou. The results of the water allocations and benefits have been analyzed. The symptoms of results indicate the higher profit with a higher wastewater treatment rate. It also represents that this model has enhanced abilities and it is relevant and practical as well as can serve as a guide for the development of this regional economy. [ABSTRACT FROM AUTHOR]