Abstract Nowadays, the research for chaotic dynamics in the social science has been interested by many researchers. This paper proposes a fuzzy controlling method for dynamic confliction model between certain group A and group B based upon love affairs model. In order to control the confliction between group A and group B, we apply control input with the symmetry triangular fuzzy numbers, which are

Abstract This article proposes a framework to deal with incomplete information in multiple criteria group decision making with intuitionistic fuzzy soft set. To do that, the weighted sum method for choice values and simple mathematical expectation method are extended to the case of obtained fuzzy soft set, and they are proved to have the same result in estimating the incomplete information. In order

Abstract In this paper, Decomposed Fuzzy Systems (DFS) structure is applied to design single intersection signal fuzzy controller. The DFS structure is to decompose each fuzzy variable into layers of fuzzy systems and each layer is to characterize one traditional fuzzy set. DFS adjusts the fuzzy membership function, the leading part, and enriches the fuzzy rule base through structural changes, thus

Abstract In risk assessment problems, multiple experts are often involved. On many occasions, assessment experts cannot give crisp scores even if there are scoring standards because of limitation and uncertainty. It is reasonable that the assessment data are expressed in the form of interval numbers with self-confidence. A fuzzy risk assessment model based on interval numbers with self-confidence is

Abstract This paper deals with the problem of the fault-tolerant fuzzy master–slave systems synchronization through an adaptive event-triggered scheme (AETS). First, a Takagi–Sugeno (T–S) fuzzy model is employed to represent the master–slave system dynamics. Second, an AETS is introduced to judge whether the newly sampled controller’s signals should be released to the slave system or not. Consequently

Abstract This paper proposes an optimal indirect approach for asymmetric bounds in constraint-following in mechanical systems with (possibly fast) time-varying fuzzy uncertainty. The uncertainty is described with fuzzy set theory. We aim at optimal controller to drive the constraint-following error of the concerned fuzzy system to lie within a desired (possibly asymmetric) bound all the time and get

Abstract In this study, a new approach based on fuzzy cognitive map (FCM) and neuro-fuzzy inference system (NFIS), called the neuro-fuzzy cognitive map (NFCM), is proposed. Here, the NFCM is used for diagnosis of autoimmune hepatitis (AIH). AIH is a chronic inflammatory liver disease. AIH primarily affects women and typically responds to immunosuppressive therapy with clinical, biochemical, and histological

Abstract In this paper, a new integral inequality lemma along with an appropriate Lyapunov–Krasovskii functional (LKF) is proposed for fuzzy time-delay system to enhance the delay upper bound estimate. The proposed lemma is referred to hereafter as Extended Affine Wirtinger inequality. The novelty of the lemma is twofold, it has the ability to integrate uncertain delay information utilizing the convex

Abstract The trade-off between computation time and predictive accuracy is important in the design and implementation of clinical decision support systems. Machine learning techniques with incremental updates have proven its usefulness in analyzing large collection of medical datasets for diseases diagnosis. This research aims to develop a predictive method for heart disease diagnosis using machine

Abstract Problems in daily life can be modeled into mathematical forms, one of them is the optimization of the quadratic form where the objective functions are the quadratic function. There are many real problems involving variables that cannot be stated numerically so that fuzzy logic appears. The purpose of this research is to optimize the quadratic form with all its parameters in the form of a fuzzy

Abstract This paper displays how to use fuzzy inference system (FIS) to control the individual uniform diversity for differential evolution algorithm (DE). DE solves nonlinear optimization problems, and a successful control mechanism for population diversity enhances the performance of DE. This study proposed a control mechanism that contains a novel mutation strategy and FIS because FIS is suitable

Abstract Effort estimation is a fairly researched field in the area of software engineering. Algorithmic and non-algorithmic methods are the two popular ways of estimating software development efforts. Various machine learning techniques are also being used to determine project efforts based on the historical project-related dataset. These techniques consume an array of project characteristics to estimate

Abstract Hand gesture is one of the most intuitive and natural ways for human to communicate with computers, and it has been widely adopted in many human–computer interaction applications. However, it is still a challenging problem when confronted with complex background, illumination variation and occlusion in real-world scenarios. In this paper, a two-stage hand gesture recognition method is proposed

Abstract Marine sonar image noise absorption under water can result in poor image quality, low image resolution and blurred target contour. This paper proposes a systematic framework including underwater image fuzzy preprocessing method, and clustering based on the principal component analysis (PCA) of underwater targets. Underwater images were denoised adopting fast median filtering algorithm with

Abstract To yield well-suited image segmentation results, conventional clustering algorithms depend on customized hand-crafted features as well as an appropriate initialization process. This latter aspect is a mandatory pre-requisite for convergence of the algorithm, in other words, its efficiency impacts the quality of the result. In this work, we introduce an Intuitionistic Fuzzy C-Means clustering

Abstract Today’s modern decision-making problem is designated by not being the most effective fuzziness; however, additionally partial reliability also plays a crucial role. The incomplete and unreliable information may also affect the selection maker to earn inaccurate decisions, ensuing in monetary losses and wastes of resources. Thus, it is vital to describe the reliability of the facts. To cope

Abstract Currently, there are many types of conversational agents whose goal is to emulate human behavior. These agents offer more believable conversations when their responses come from a deliberative process that mimics individuals’ character. Conversational agents are mainly used for response selection linguistics and context situated strategies. These approaches usually build rules to find answers

Abstract In decision-making, the multiobjective portfolio selection problem (MPSP) consists of the selection of alternatives based on preferences of a particular decision-maker (DM). In real-world applications, MPSP has several conflicting criteria that DMs must consider to determine an appropriate solution. So far, only fuzzy outranking relations have been used in a relational system of preferences

Abstract In this paper, we investigate the probabilistic linguistic multiple attribute group decision-making (MAGDM) with incomplete weight information. In this method, the linguistic term sets (LTSs) is converted into probabilistic linguistic term sets (PLTSs). For deriving the weight information of the attribute, an optimization model is built on the basis of the fundamental idea of conventional

In the original publication the author affiliations were inconsistent. The correct affiliations are provided in this correction.

Abstract Evidential reasoning (ER) approach is a representative method for analyzing uncertain multi-criteria decision-making (MCDM) and multi-criteria group decision-making (MCGDM) problems. Its core is ER algorithm used to combine belief distributions on criteria, which is developed based on Dempster’s rule of combination and probability theory. The ER algorithm is nonlinear and more computationally

Abstract Fuzzy approaches that are proposed to describe uncertain, impressive or vague concepts, are based on the construction of membership function (MF), which reflects what is known about the linguistic variables in the application domain. However, a non-trivial problem exists in how to construct the most appropriate MF that has the best-fit representation of the analysed problem. Therefore, many

Abstract This paper focuses on linguistic classification of scene texts in natural scene images. In this paper, an attempt is made to localize texts based on multi-level thresholding by fuzzy-based Renyi entropy. Complex natural scene images with diversified challenges are considered. A set of heuristic rules comprising geometric filters and stroke width transform govern the process of locating potential

Abstract Recently, novel approaches have been developed for simulating bubbly flow as well as distributed and constant phase evolution by means of a two-phase reactor. Among these approaches, the Eulerian–Eulerian method and soft computing approaches can be mentioned. Since complex numerical methods (for example, multidimensional Eulerian–Eulerian method) require several runs for fluid conditions optimization

Abstract As a typical model of three-way decisions (3WDs), decision-theoretic rough set (DTRS) has received extensive attention from researchers in the decision-making fields. Intuitionistic uncertain linguistic variables (IULVs) combine the advantages of intuitionistic fuzzy sets (IFSs) and uncertain linguistic variables (ULVs), IULV is more flexible in dealing with uncertain information in decision-making

Abstract In order to accurately determine the auto insurance rate of UBI, this paper proposes to use fuzzy controller to calculate the rate and optimize it by using the simulated annealing particle swarm algorithm with Metropolis criterion. Firstly, a fuzzy controller is constructed by selecting monthly mileage and violation times to calculate the self-underwriting coefficient. In order to eliminate

Abstract In this study, a novel approach, interval fuzzy c-regression models with competitive agglomeration (IFCRMCA), is proposed to deal with the symbolic interval-valued data. The proposed IFCRMCA approach can identify the partition of the interval-valued data using both the distances to the cluster centers and the errors of interval regression models for each cluster. Due to the concepts of competitive

Abstract In this paper, a novel adaptive fuzzy controller is developed for the uncertain fractional-order switched nonlinear systems whose output is quantized by a class of sector-bounded quantizers. Since the states are not completely measurable, an observer with quantized output signal is designed to estimate the unknown system states. Meanwhile, based on the fractional Lyapunov stability criterion

Abstract This paper investigates the dissipative interval type-2 (IT2) fuzzy control issue for a group of uncertain nonlinear systems subject to randomly occurring sensor saturation and quantization. By utilizing the both lower and upper membership functions with relevant tradeoff moduli, uncertainties, which exist in the considered systems, can be captured effectively. During the design of the IT2

Abstract Multi-attribute decision-making approach is a widely used algorithm that needs some aggregation tools and several such aggregation operators have been developed in past decades to serve the purpose. Hamacher aggregation operator is one such operator which is based on Hamacher t-norm and t-conorm. It is observed that the Hamacher aggregation operators of intuitionistic fuzzy set, Pythagorean

Abstract Online customer reviews (OCRs) provide much information about products or service, but the mass of information increases the difficulty for customers to make decisions. Thus, we establish a multi-criteria decision making (MCDM) model to evaluate products or service. To analyze OCRs, the sentiment analysis (SA) is introduced to identify the sentiment orientation of reviews. Considering that

Abstract Fuzzy uncertainty (FU) exists widely in engineering applications, but there lack design optimization methods under FU, thus a credibility-based design optimization (CBDO) is focused to obtain the safety design under FU in this paper. Firstly, the concepts of credibility index and most credible point (MCP) are presented to measure the safety degree under FU, where the credibility index and

Abstract One of the popular area of research for the past decade in the academia as well as in the industry is networked control systems (NCS). Variable time delays induced by the network and data packet dropout during transmission of data are among the problems encountered in these type of systems. Researchers proposed and developed various control strategies over the past years to deal with the above-mentioned

Abstract The intuitionistic fuzzy numbers (IFNs) have been extensively studied in recent years. However, the traditional operational rules (ORs) of the IFNs still have some drawbacks in solving the practical decision-making problems. Einstein t-conorm and t-norm (TAT) are an important and typical class of the TAT, but the ORs for the IFNs based on the Einstein TAT (ETAT) cannot consider the interaction

Abstract In this paper, we consider interval multi-objective linear programming (IMOLP) models which are used to deal with uncertainties of real-world problems. So far, a variety of approaches for obtaining efficient solutions (ESs) of these problems have been developed. In this paper, we propose a new and two generalized methods. In the new method, converting IMOLP into an interval linear programming

Abstract Interest rate is an important part of option pricing model, which attracts many scholars’ attention. In this paper, the fuzzy interest rate model is established under the framework of credibility theory, and the fuzzy term structure equations of zero coupon bonds and coupon bonds are derived. In order to solve the fuzzy term structure equations, the conditions of bond price possessing affine

Abstract The short-term power load forecast is deeply studied by integrating fuzzy BP neural networks and composite adaptive filtering in this paper. Due to the difficulty on accurate modeling of complex factors in smart grid, the fuzzy technology is introduced to deal with the uncertain factors. Meanwhile, data-driven method, such as neural networks, is used as the basic frame of the power load forecast

Abstract This paper proposes a data envelopment analysis (DEA)-based portfolio efficiency evaluation approach integrated with a rebalancing method to help investors acquire efficient portfolios. Two fuzzy portfolio selection models with value at risk (VaR) and conditional value at risk (CVaR) as objectives are proposed under the credibilistic framework. The models are constrained by realistic constraints

Abstract Air quality control is necessary to improve the environmental condition in particular Indoor Air Quality (IAQ), which has a direct impact on a living organism. In marine application, the International Maritime Organization (IMO) made standards to determine the gas emission limits and specify the air circulation in IAQ in addition to employing in energy-efficient and energy management system

Abstract Fuzzy neural networks, with suitable learning strategy, have been demonstrated as an effective tool for online data modeling. However, it is a challenging task to construct a model to ensure its quality and stability for non-stationary dynamic systems with some uncertainties. To solve this problem, this paper presents a novel identification model based on recurrent interval type-2 fuzzy wavelet

Abstract This article is an advanced approach to intuitionistic fuzzy set through application of cubic set theory. For instance, we establish the idea of the intuitionistic cubic fuzzy set (ICFS) theory and define several operations for ICFS ; also establish a series of weighted aggregation operators under intuitionistic cubic fuzzy information, so-called intuitionistic cubic fuzzy weighted averaging

Abstract Considering the undeniable role of websites in the success of interactions between citizens and governments, evaluating e-government web sites is a worthwhile topic. This research has developed a multi-attribute-decision-making technique rely on Step-Wise Weight Assessment Ratio Analysis and hesitant fuzzy weighted geometric Heronian mean decision-making methods, for evaluating e-government

Abstract This study presents a comparative evaluation of interval type-2 intuitionistic fuzzy logic system using three derivative-based learning algorithms on noisy regression problems. The motivation for this study is to manage uncertainty in noisy regression problems for the first time using both membership and non-membership functions that are fuzzy. The proposed models are able to handle ‘neither

Abstract A novel hybrid soft set model called intertemporal hesitant fuzzy soft set is proposed, and then, it is showed how it can deal with group decision making problems. This model can be used to incorporate hesitant information that varies across time, when the alternatives are described by their degree of agreement with various characteristics. According to the period of time (whether it is indefinitely

Abstract In this paper, a T–S (Takagi–Sugeno) adaptive tracking algorithm control based on small gain theorem is proposed for an uncertain robot system with \(n\)-link manipulators. A nonzero time-varying parameter is introduced in the common T–S fuzzy logic system, the T–S type fuzzy logic system with updated parameters laws is build, then the new and original universal approximation with parameter

Abstract As a solution method that not only considers the probability distribution information of data, but also ensures that the results are not too conservative, more and more researches have been made on the distributionally robust optimization method. Based on the minimum cost consensus model, this paper proposes a new minimum cost consensus model with distributionally robust chance constraints