Iranian journal of fuzzy systems
Volume:17 Issue: 5, Sep Oct 2020

In this paper, we study linear fuzzy time-variant optimal control systems using the generalized differentiability concept and we present the general form of optimal controls and states. Some examples are provided to illustrate our results.

The classical ergodic theory hasbeen built on σ-algebras. Later the Individual ergodictheorem was studied on more general structures like MV-algebrasand quantum structures. The aim of this paper is to formulate theIndividual ergodic theorem for intuitionistic fuzzy observablesusing  m-almost everywhere convergence, where  m is anintuitionistic fuzzy state. We show the Kolmogorov constructionfor intuitionistic fuzzy observables, too.

In this contribution we define a new class of non-linear integrals based on decomposition integrals. These integrals are motivated by greediness of many real-life situations. Another view on this new class of integrals is that it is a generalization of both the Shilkret and PAN integrals. Moreover, it can be seen as an iterated Shilkret integral. Also, an example in time-series analysis is provided.

Throughout this paper, our  main idea is to analyze the Arrovian approach in a fuzzy context, paying attention to different extensions of the classical Arrow's model arising in mathematical Social Choice to aggregate preferences that the agents define on a set of alternatives. There is a wide set of extensions. Some of them give rise to an impossibility theorem as in the Arrovian classical  model. But others lead to possibility results. We explore the main grounds that lead to impossibility or possibility. In this analysis,   representative examples arise. One of them corresponds to an impossibility result, and the other ones allow the aggregation  of fuzzy individual preferences to an individual one. We introduce new techniques for the proofs, specially for the one that leads to impossibility.

Probabilistic or stochastic programming is a framework for modeling optimization problems that involve uncertainty.In this paper, we focus on multi-objective linear programmingproblems in which the coefficients of constraints and the righthand side vector are fuzzy random variables. There are several methodsin the literature that convert this problem to a stochastic orfuzzy problem. By using a special type of fuzzy inequality, wetransform the problem into a convenient stochastic problem. Thensome known methods are applied to obtain the optimal solution.Finally, the equivalent multi-objective problem is solved by aninteractive approach. A numerical example is provided to illustrate the procedure.

In this paper, we are discuss about the issue of synchronization for singular complex dynamical networks with Markovian jumping parameters and additive time-varying delays through pinning control by Takagi-Sugeno (T-S) fuzzy theory.The complex dynamical systems consist of m nodes and the systems switch from one mode to another, a Markovian chain with glorious transition probability. Based on the control strategies are designed, the singular complex dynamicalnetworks are synchronized. A new class of Lyapunov-Krasovskii functional, which contains integral terms is constructed to derive the stability criteria. Some sufficient conditions for synchronization in the form of linear matrix inequality(LMI) approach. Finally, numerical example is presented to support the main results of this paper.

Control charts are widely used in industrial processes as well as in health sciences and particularly for monitoring the performance of cardiac surgeon or a group of surgeons based on the preoperative risk of patients. Since the preoperative risk is a vague and nonprecise variable and the anesthesiologists after checking how many risk factors a patient has, determine the risk of mortality before the surgery as a linguistic term such as low, medium, high or others like that, it is better to be considered as a fuzzy number, which can be determined by using fuzzy regression models. In this condition, we need a special chart to monitor the performance of surgeons based on these fuzzy data. In this article, we propose risk-adjusted control charts based on LR-fuzzy data and then test our conclusions on real data.

This paper,  applies the concept  of KM-fuzzy metric spaces and  introduces a novel concept of KM-fuzzy metric  graphs based on KM-fuzzy metric spaces.  This study, investigates the finite KM-fuzzy metric spaces with respect to metrics and KM-fuzzy metrics and constructs KM-fuzzy metric spaces on any given non-empty sets. It tries to  extend   the concept of KM-fuzzy metric spaces to  a larger class of KM-fuzzy metric spaces such as  union and product of KM-fuzzy metric spaces and in this regard  investigates a class of  products of KM-fuzzy metric  graphs.

This article presents a fuzzy sliding-mode control scheme for a class of Takagi-Sugeno (T-S) fuzzy which are subject to norm-bounded uncertainties in each subsystem. The proposed stabilization method can be adopted to explore T-S uncertain fuzzy systems (TSUFS) with various local control inputs. Firstly, a new design is proposed to transform TSUFS into sliding-mode dynamic systems.In addition, the proposed stabilization condition for the sliding-mode dynamic system can be formulated in terms of linear matrix inequalities (LMIs) using the Lyapunov functions. Finally, the validity of the proposed design strategy is demonstrated through the simulation of an inverted pendulum on a cart. According to simulation results, the performance indexes of the proposed method can achieve satisfactory results with minimum energy control effort, faster response, and zero steady-state error.

In this paper, three types of (philosophical, optimistic and pessimistic) multigranulation single valued neutrosophic (SVN) covering-based rough set models are presented, and these three models are applied to the problem of multi-criteria group decision making (MCGDM).Firstly, a type of SVN covering-based rough set model is proposed.Based on this rough set model, three types of multigranulation SVN covering-based rough set models under the concept of multigranulation SVN $beta$-covering approximation space are proposed, where $beta$ is a SVN number.Moreover, the connections among these four models are investigated.Secondly, some conditions under which different multigranulation SVN $beta$-covering approximation spaces induced the same multigranulation SVN covering approximation operators are presented.Finally, three novel methods are presented to MCGDM problems under the multigranulation SVN covering-based rough set models.Furthermore, the proposed MCGDM methods are compared with other methods through a problem about paper defect diagnosis.

In testing hypotheses, we may confront with cases where data are recorded as non-precise (fuzzy) rather than crisp. In such situations, the classical methods of testing hypotheses are not capable and need to be generalized. In solving the problem of testing hypotheses based on fuzzy data, the fuzziness of the observed data leads to the fuzzy p-value. This paper has been focused to calculate fuzzy p-value based on fuzzy data using the extension principle. Also, considering that p-value method is the most widely used / popular approach for testing hypotheses among different sciences users, two fuzzy $p$-value-based case studies have been provided in this paper. The first case study is discussed on ″the fuzzy data from a speedometer camera" and the second is deliberate about ″the lifetime of produced batteries in a factory" and both of them have been solved by a novel approach considering other studies found in the literature.

This paper introduces new similarity classifiers using the Heronian mean, and the generalized Heronian mean operators. We examine the use of these operators at the aggregation step within the similarity classifier. The similarity classifier was earlier studied with other operators, in particular with an arithmetic mean, generalized mean, OWA operators, and many more. The two classifiers here are tested on four real world data sets, i.e., echocardiogram, fertility, horse-colic, and lung cancer. Three previously studied similarity classifiers are used as benchmarks to the new approaches. We observe that the similarity classifier with a generalized Heronian mean produces good classification results for the tested data sets, and is therefore more suitable for use in these classification problems.

Multi-criteria group decision-making is a process in which decision makers assess the performance of alternatives on the basis of conflicting criteria to opt the most worthy alternative as solution. TOPSIS and ELECTRE are effective and commonly used methods to solve multiple criteria decision-making problems. The aim of this study is to propose two new models, namely, complex Pythagorean fuzzy TOPSIS (CPF-TOPSIS) method and complex Pythagorean fuzzy ELECTRE I (CPF-ELECTRE I) method, to tackle multiple criteria group decision-making problems comprising complex Pythagorean fuzzy data. In these methods, we compare complex Pythagorean fuzzy numbers on the basis of their score functions. We use revised closeness index for the ranking of alternatives in CPF-TOPSIS method. {In complex Pythagorean fuzzy concordance and discordance sets, we compare the alternatives on being superior and inferior to other alternatives on the basis of score degree, accuracy degree and indeterminacy. In CPF-ELECTRE I method, we use outranking decision graph to obtain the best alternative.} We illustrate the structure of both methods with the help of flow charts. To verify the accuracy of {proposed methods}, we present an explanatory example for selection of best interior designer for a hotel renovation. {We authenticate the proposed techniques by providing a brief comparative analysis of these methods with existing methods}.

Generalized trapezoidal fuzzy numbers (GTFNs) have been widely applied in uncertain decision-making problems. The similarity between GTFNs plays an important part in solving such problems, while there are some limitations in existing similarity measure methods. Thus, based on the cosine similarity, a novel similarity measure of GTFNs is developed which is combined with the concepts of geometric distance, center of gravity, area and perimeter after analyzing the limitations of previous methods. Then comparative analysis is conducted with existing similarity measures, and the results show that the novel similarity measure has better distinguishability and lower invalidity. Furthermore, a general process, which combines the new similarity measure of GTFNs with compromise methods, is developed to deal with multi-attribute group decision making (MAGDM) problem. Finally, we combine fuzzy VIKOR with the general process as illustrated example, which proves the superiority of the developed similarity measure in solving MAGDM problem.

Investment appraisal in advanced manufacturing technologies (AMTs) has been receiving considerable attention over the past three decades. As stated in numerous studies, traditional engineering economic methods cannot adequately justify investments in AMTs. Thus, beside these methods, some other solutions have been proposed in this field. The methods applied in the evaluation of AMTs can be classified into four groups including economic, strategic, analytical and integrated methods. In this paper, an integrated model is presented for the evaluation of AMTs. The model is developed according to a fuzzy multi-criteria decision making procedure. It is assumed that a group of decision makers evaluate different AMT alternatives according to certain strategic and economic criteria. The importance of the criteria and the weights of the alternatives versus the criteria are expressed using linguistic variables set by the decision makers. To compute the economic criterion of each alternative, a linear program with chance constraints is proposed. Finally, using different fuzzy operators, the AMT alternatives are scored and ranked. In this regard, an example is presented to show the performance of the model.