Diffeomorphism-transformed adaptive robust control for dual-arm collaborative robot systems with inequality constraints.

Recent work has shown that the f-index of inclusion can serve as a foundation for modeling Generalized Modus Ponens. In this paper, we develop a novel fuzzy inference system based on this inference rule. To establish its soundness, we connect it to a Fuzzy Description Logic LU enriched with fuzzy modifiers (also known as fuzzy hedges). This logic background provides to the approach a strength absent in most fuzzy inference systems in the literature, which allows us to formally prove a series of results that culminate in a final correctness theorem for the proposed fuzzy inference system. This paper also presents a running example aimed at showing the potential applicability of the proposal.

The article considers the problems of decision-making transparency in the presence of classification in data sets. A method for searching for and explaining hidden patterns in data is proposed, taking into account the scales of feature measurements. The values of quantitative features in the optimal boundaries of non-overlapping intervals are replaced by gradations of nominal ones. Unification of measurement scales is used to calculate the values of the membership function to classes by the frequency of occurrence of gradations of features in them. Selective deletion of feature values or introduction of gaps in the data is used to change the boundaries of intervals. The purpose of such actions is to increase confidence in the inference rules. It is proved that the presence of gaps in the data does not lead to a significant loss in the accuracy of the classification results. Three types of linguistic variables are introduced to describe confidence in the classification results, the informativeness of a set of features, and specific properties of features associated with the subject area.

Design for disassembly (DFD) is a basic design technology serving the scrap and recycling stages, which can relieve the environmental pressure and improve the economic benefits. Considering the wide variety of mechanical products and the shortening of product lifecycle, the product design integrating customer requirements can effectively improve the market competitiveness of products. Thus, this paper proposes a QFD-based human-centric decision-making approach for disassembly scheme selection. The theory of the interval 2-tuple q-rung orthopair fuzzy sets (I2q-ROFSs) is proposed to better describe the ambiguity of the environment and avoid information loss/distortion in the information aggregation stage. A scheme evaluation system based on the disassembly technical features is established. A hybrid multi-attribute decision-making (MADM) method combing BWM-FQFD (best worst method and fuzzy quality function deployment) and RT-EDAS (the regret theory based on distance from average solution) is presented to obtain the optimal alternative. A case study, i.e., four refrigerator DFD schemes, is applied to verify the effectiveness of the proposed method. The result demonstrates that this work provides an effective tool to select the optimal scheme of disassembly considering customer requirements and some references for designers <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The selection of DFD schemes is the last task of the disassembly design. The decision information of the disassembly design scheme has insufficient evaluation complexity ability and the information distortion of the evaluation information in the aggregation stage. To end this, this paper proposes I2q-ROFSs which is a fuzzy set with the higher level of fuzzy description and avoiding information distortion. At present, the disassembly design research lacks the correlation between customer demand and disassembly technology features. Therefore, a fuzzy QFD method can well assist manufacturing enterprises to improve the market competitiveness of their products. Then, RT-EDAS can describe consumer psychology, which can choose a more market-appropriate DFD scheme.

The Internet of Things (IoT) has become one of the most popular technologies in recent years. Advances in computing capabilities, hardware accessibility, and wireless connectivity make possible communication between people, processes, and devices for all kinds of applications and industries. However, the deployment of this technology is confined almost entirely to tech companies, leaving end users with only access to specific functionalities. This paper presents a framework that allows users with no technical knowledge to build their own IoT applications according to their needs. To this end, a framework consisting of two building blocks is presented. A friendly interface block lets users tell the system what to do using simple operating rules such as "if the temperature is cold, turn on the heater." On the other hand, a fuzzy logic reasoner block built by experts translates the ambiguity of human language to specific actions to the actuators, such as "call the police." The proposed system can also detect and inform the user if the inserted rules have inconsistencies in real time. Moreover, a formal model is introduced, based on fuzzy description logic, for the consistency of IoT systems. Finally, this paper presents various experiments using a fuzzy logic reasoner to show the viability of the proposed framework using a smart-home IoT security system as an example.

Minimizing fuzzy interpretations in fuzzy description logics by using crisp bisimulations

We consider comparative dissimilarity relations on pairs on fuzzy description profiles, the latter providing a fuzzy set-based representation of pairs of objects. Such a relation expresses the idea of “no more dissimilar than” and is used by a decision maker when performing a case-based decision task under vague information. We first limit ourselves to those relations admitting a weighted Lp distance representation, for which we provide an axiomatic characterization in case the relation is complete, transitive and defined on the entire space of pairs of fuzzy description profiles. Next, we switch to the more general class of comparative dissimilarity relations representable by a Choquet Lp distance, parameterized by a completely alternating normalized capacity.

During the analysis of economic resilience of enterprises and industries within existing scientific approaches based on fuzzy sets and soft computing methodology, a number of problems have arisen that require solutions, namely: a) making adjustments to existing linguistic norms of resilience factors; b) incorporating mechanisms of intelligent filtering when forming industry indices; c) improving the accuracy of decision models embedded in the corresponding strategic map. This article describes a series of problematic cases that can be improved within the proposed approaches, including by: a) transitioning from interval membership functions in the classifier to trapezoidal ones; b) excluding noise-contaminated context from the sample that has been rejected based on formal criteria; c) phasifying decision parameters on the strategic map as part of a 4х6 matrix. In the latter case, the technology of pairwise comparison of fuzzy numbers is applied to assess the risk of inefficiency in the decision-making process. It can be formulated that in solving problems related to fuzzy descriptions, we have proposed a series of solutions using the same fuzzy descriptions, but in a more advanced version. In fact, we suggest upgrading a number of previously proven approaches for analyzing economic resilience within the fuzzy scientific paradigm.

This paper explores the application of fuzzy goal programming (FGP) for modeling and solving land allocation problems with chance constraints, aimed at optimizing the production of seasonal crops within agricultural systems in uncertain environments. The decision-making environment in agricultural systems is often characterized by imprecision, particularly due to unpredictable rainfall patterns and limited availability of irrigation water, which are influenced by socio-economic factors. These uncertainties pose significant challenges in real-world agricultural scenarios. The proposed model incorporates fuzzy descriptions for the utilization of cultivable land, farming resources, and the achievement of production goals for seasonal crops. Water supply, as a critical resource, and socio-economic constraints are represented probabilistically within this uncertain decision-making framework. The model addresses land-use planning for the cultivation of five major crops—Paddy, Wheat, Mustard, Potato, and Pulses—across three crop cycles: Pre-Kharif, Kharif, and Rabi, throughout the planning year for the Bardhaman district of West Bengal, India. The solution process focuses on maximizing the membership value (unity) of the fuzzy goals, reflecting the aspirations of the decision maker (DM) as closely as possible, based on their needs and objectives. The potential of this approach is demonstrated through a case study of Bardhaman district, West Bengal, India, illustrating how FGP can be used to address agricultural planning challenges under uncertain conditions.

The object of the study is the control of the movement of a mobile robot along a line. To build automatic control systems for robots, various algorithms are used, for example, PID controllers, as well as algorithms based on fuzzy logic methods. Methods of the fuzzy theory form one of the universal approaches to the analysis and modeling of applied problems, including in matters of robot control.&#x0D; In any study, problems arise immediately as soon as events (facts) are accepted as fuzzy, for example, due to inaccurate measurement, fuzzy description, etc. The concept of fuzziness is intuitively clear to everyone, but its formalization causes difficulties, which are always of a fundamental nature. Consideration of the foundations of the theory is caused by the search for possible solutions to problems in the experimental part of the study, which addresses the issues of speed regulation and dynamic accuracy of the movement of a mobile robot.&#x0D; The article puts forward a hypothesis about the effectiveness of the method of controlling the movement of a mobile robot based on the fuzzy theory in comparison with the PID controller. To substantiate the hypothesis, a description of the movement of the robot in terms of fuzzy theory is given, the features of the movement of the robot depending on the type of tracks are considered, a fragment of the program for implementing a controller based on fuzzy logic is presented, and a number of experiments are carried out.&#x0D; Experimental verification showed a more stable and faster movement of the robot along the tracks and confirmed the hypothesis of the study. The results of the experiments made it possible to correct the fuzzy inference rules and the robot motion control program. The results obtained open up new possibilities for the wide application of fuzzy theory in robotics.

Fuzzy ontology-based approach for liver fibrosis diagnosis

Three-way decision for three-stage ranking pattern with criterion fuzzy concept

A sustainable economy is a complex issue related to economic, social and environmental areas. For European Union (E.U.) countries, it is closely linked to the issues of sustainable industry, infrastructure and innovation in R&D. Thus, the article is specifically focused on identifiers of Sustainable Development Goal 9 (S.D.G. 9) created by E.U. To meet the main targets based on sustainable development and The European Green Deal strategy, it is necessary to have an idea of the possible future development of the S.D.G. 9 indicators. The main aim of this article is to create a semi-deep prediction model using cluster analysis and fuzzy approach. The contribution of this article is the use of a fuzzy approach to create a multivariate prediction model that allows to circumvent the limitations of classical regression analysis. The E.U. countries were divided into five clusters. A semi-deep prediction model was created for each cluster using fuzzy approach.

Fuzzy description logics (FDLs) are useful for dealing with fuzzy terminological knowledge for domains with linked data. Logical similarity or indiscernibility between individuals with respect to a given FDL is a fuzzy measure, which becomes crisp when the logic is extended with the Baaz projection operator. The measure is closely related to bisimulation. While logical indiscernibility is defined semantically, its corresponding notion based on bisimulation enables the computation. In this article, we study crisp bisimulations between fuzzy interpretations in FDLs with the Baaz projection operator under a general semantics based on an abstract algebra of fuzzy truth values. We define such bisimulations for a large class of FDLs with a rich set of well-known concept and role constructors, including qualified/unqualified number restrictions, nominals and the role constructors that correspond to the program constructors of propositional dynamic logic. We formulate and prove their logical characterizations, including the invariance of concepts under crisp bisimulations and the Hennessy–Milner property of crisp bisimulations. Such logical characterizations do not depend on a concrete semantics, such as the Gödel, Łukasiewicz, and product semantics. Based on crisp bisimulations, we also study indiscernibility of individuals in FDLs with the Baaz projection operator. An interesting consequence of our results states that, when restricting to the considered FDLs and image-finite fuzzy interpretations that are witnessed and modally saturated, indiscernibility of individuals is independent from the underlying algebra of fuzzy truth values in the case without number restrictions, and it is the same for both the Gödel and product semantics in the case with number restrictions.

Building the right learning teams is a key to the success of collaborative learning in online and offline learning environments. However, existing research on learning team formation (LTF) ignores the uncertainty of learners' abilities and lacks a common problem modeling and optimization approach. Aiming at the characteristics of two typical types of leader-centered (LC) LTF problems, a universal optimization framework of LC-LTF is proposed by introducing role-based collaboration (RBC) theory. This framework evaluates the comprehensive ability of learners via a fuzzy description mechanism; applies the environments–classes, agents, roles, groups, and objects (E-CARGO) model to formulate the LC-LTF problem; and employs an optimization platform to obtain an optimal solution. A case study demonstrates the effectiveness and feasibility of the proposed framework.

This article develops a diffeomorphism-based robust bounded control (DRBC) for the permanent magnet linear synchronous motor system subject to inequality constraints (i.e., bounded input and position constraints) and uncertainties. The uncertainties, including parameter uncertainties and external disturbances, are potentially nonlinear and fast time varying. The bound of the uncertainty is described by a fuzzy set. To overcome the bounded input constraint, a robust bounded control is proposed based on a novel input diffeomorphism scheme, which is in a deterministic form and not if–then rule based. Furthermore, to overcome the bounded position constraint, a transformed system is formulated by a state diffeomorphism scheme, which transforms the bounded state-constrained system to an unconstrained one. Thus, the output of the controlled system can be restricted to a prescribed range. The DRBC guarantees both the uniform boundedness and the uniform ultimate boundedness of the transformed system. A fuzzy performance index, which combines the steady-state performance (the average fuzzy performance) and control effort, is then established based on the fuzzy description of uncertainty. As a result, the design parameter optimization can be solved by minimizing the performance index. Experimental results demonstrate that the DRBC is of superior tracking performance and robustness without violating the prescribed inequality constraints.

The paper considers an approach to the construction of fuzzy structured numerical sets based on the formation of a fuzzy original of a special type with subsequent replication on the number axis. The constructive principle of fuzzy original formalization is to define a fuzzy triangular number with the appropriate support. A variant of the formation of fuzzy numerical sets, formalizing the "fast" and "slow" passage of time, is proposed. The developed technique allows us to propose a solution to the problem associated with the formalization of the subjective perception of the time reference in processes involving a person, to obtain results and evaluate the impact of fuzzy description and taking into account the dynamics of the time reference on the solution of various optimization problems. A general approach to the formation and solution of fuzzy linear optimization problems is considered, the transition from fuzzy to parametric formulation in the Bellman-Zade form is described. Examples of the use of fuzzy flow of time for different statements of tasks that arise when determining the order of the set of tasks within a given time interval with or without additional restrictions on the execution process are considered. An approach is proposed for the correction of the initial time distribution plans, taking into account different rates of time counting. A mathematical model of the fuzzy traveling salesman problem is formulated as a problem of finding a route to visit a given number of cities without repetitions with a minimum travel time with time parameters specified as right fuzzy numbers, the support value in which depends on various external conditions and factors. The results of calculations of solutions of fuzzy optimization problems are obtained. The influence of the speed of the passage of time on the solution of the considered optimization problems is determined.

Abstract Shared conceptualization, in the sense we take it here, is as recent a notion as the Semantic Web, but its relevance for a large variety of fields requires efficient methods of extraction and representation for both quantitative and qualitative data. This notion is particularly relevant for the investigation into, and construction of, semantic structures such as knowledge bases and taxonomies, but given the required large, often inaccurate, corpora available for search we can get only approximations. We see fuzzy description logic as an adequate medium for the representation of human semantic knowledge and propose a means to couple it with fuzzy semantic networks via the propositional Łukasiewicz fuzzy logic such that these suffice for decidability for queries over a semantic-knowledge base such as “to what degree of sharedness does it entail the instantiation C(a) for some concept C” or “what are the roles R that connect the individuals a and b to degree of sharedness ε.”

Computing the fuzzy partition corresponding to the greatest fuzzy auto-bisimulation of a fuzzy graph-based structure under the Gödel semantics

Метод определения объекта из ограниченной выборки по нечеткому описанию на естественном языке