This paper addresses the designing of an adaptive type-2 fuzzy PID (T2FPID) controller for load frequency control (LFC) of an interconnected nonlinear power system considering input time-delays. The input delay, which can commonly be raised by failure in the communication links and actuator operating lag may lead to control system instability. In the proposed adaptive T2FPID controller, output scaling

The location of the electric vehicle charging station (EVCS) is the important link in the construction of the EVCS. The optimal location of the electric vehicle directly affects the operating efficiency of the charging station and the satisfaction of the electric vehicle user, site selection play an important part throughout whole life cycle, which is deemed to be multiple attribute group decision

The adaptive neuro-fuzzy inference system (ANFIS) is employed in a vast range of applications because of its smoothness (by Fuzzy Control (FC)) and adaptability (by Neural Network (NN)). Although ANFIS is better in nonlinear optimization, two major loopholes need to be addressed thoroughly. They are the curse of dimensionality and computational complexity. To overcome these complications, a novel usage

A supercapacitor (SC)-based interior permanent magnet synchronous motor (IPMSM) drive including the speed tracking of a specific velocity profile and the charging of the SC is developed in this study to emulate the operation of an urban light rail vehicle (LRV). In the SC-based IPMSM drive, the motoring mode to emulate the LRV speed tracking control and the charging mode for the charging of the SC

Comprehensive evaluation is the key to ensuring that cloud manufacturing services can run smoothly over the whole life cycle of products. It is therefore of great importance to carry out a careful scientific evaluation of cloud manufacturing services. In this paper, a combination of qualitative and quantitative methods was adopted based on fuzzy comprehensive evaluation (FCE), and a comprehensive evaluation

In the classical leave-one-out procedure for outlier detection in regression analysis, we exclude an observation and then construct a model on the remaining data. If the difference between predicted and observed value is high we declare this value an outlier. As a rule, those procedures utilize single comparison testing. The problem becomes much harder when the observations can be associated with a

In this paper, a novel control approach is proposed for a class of uncertain nonlinear system with unmodeled dynamics. Each output of the system is modeled by several first-order dynamic fractional-order fuzzy systems. The best dynamic model is selected at a period of time and the control signal is designed based on this model. The dynamic fractional-order models are based on the special case of general

Fuzzy Q-learning extends Q-learning to continuous state space and has been applied to a wide range of applications such as robot control. But in a multi-agent system, the non-stationary environment makes joint policy challenging to converge. To give agents more suitable rewards in a multi-agent environment, a multi-agent reward-iteration fuzzy Q-learning (RIFQ) is proposed for multi-agent cooperative

The manuscript focuses on the interval type-2 (IT2) fuzzy sampled-data H∞ control problem for nonlinear systems with parameter uncertainties and random communication. Nonlinear systems are constructed as Takagi–Sugeno (T–S) fuzzy form, and the parameter uncertainties are represented by employing the membership functions. A reliable controller is designed, based on which the fuzzy closed-loop system

The adaptive interval type-2 (IT2) fuzzy event-triggered saturation control scheme is proposed for a photovoltaic power system with unknown equivalent resistances and the disturbance of the power grid voltage. As the equivalent resistors and the disturbance are uncertain, interval type-2 fuzzy logic systems (IT2FLSs) are used to handle the uncertain nonlinear dynamics. Based on backstepping control

Interval-valued intuitionistic fuzzy set (IVIFS) is a potent tool to capture the vagueness and uncertainty of information. Archimedean copula and co-copula (ACC) are crucial implement utilized to generate operational rules of interval-valued intuitionistic fuzzy numbers (IVIFNs). Meanwhile, Bonferroni mean (BM) has a distinct merit that it can take into consideration the interconnection of diverse

Fuzzy regression is a generalized regression model to represent the relationship between dependent and independent variables in a fuzzy environment. The fuzzy linear regression analysis seeks for regression models fitting well all the data based on a specific criterion. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is employed for the analysis and prediction of a nonparametric fuzzy

Decision-making trial and evaluation laboratory (DEMATEL) method is comprehensively applied in the complicated socioeconomic system. Existing literatures on DEMATEL focus on static decision environment at single time node. However, due to the increasingly complex and rapidly changing socioeconomic environment, decision-making has become more complex and dynamic. In this paper, dynamic group DEMATEL

System design deals with various challenges of targets and resources, such as reliability, availability, maintainability, cost, weight, volume, and configuration. This paper deals with the multi-objective system availability and cost optimization of parallel–series systems by resorting to the multi-objective strawberry algorithm also known as the Plant Propagation Algorithm or PPA and a fuzzy method

Recently, the soft drink industry has been confronted with a rapidly changing business climate and increased competition, creating risks and uncertainties in its supply chain. Thus, immediate actions are required to handle these risks and uncertainties. The purpose of this paper is to identify and classify these risks in the soft drink supply chain and to develop a methodology comprising a fuzzy analytical

The ranking of fuzzy numbers plays a remarkable role in some application systems such as approximate reasoning, decision analysis, optimization and forecasting under fuzzy environments. In this paper, we propose a novel possibility degree formula of ranking generalized fuzzy numbers based on the possibility theory. The combined effects of the possibilistic mean and the variance/standard deviation on

Today’s business environments are prone to high levels of uncertainties and risks because the speed of changes is too high. Hence, the way of dealing with threats has changed. Complex socio-technical systems (CSSs) are confronted with out-of-control disturbances that need risk management systems to be applied. Risk management is organized activities to monitor and control risks in CSSs. Newly, resilience

This paper addresses the problem of consensus-based state estimation for interval type-2 Takagi–Sugeno (T–S) fuzzy time-delay systems over a wireless sensor network under deception attacks, partial information exchange and switching sensor-network topologies. The switching-topology-dependent estimators are constructed using interval type-2 fuzzy models considering the cases where the premise variables

Motivated by bipolar fuzzy (\(B_{p}\)F) sets (\(B_{p}\)FSs) and picture fuzzy sets (\(P_{c}\)FSs), we introduced the aggregation operators for the novel extension of \(P_{c}\)FSs named as bipolar picture fuzzy sets (B\(P_{c}\)FSs). Firstly, various arithmetic rules, scores and accuracy functions of the B\(P_{c}\)FSs are presented. Secondly, some aggregation operators of B\(P_{c}\)FSs are constructed

Thermal energy is the energy from a substance in which molecules and atoms vibrate faster because of an increase in temperature. Thermal energy storage (TES) is an available energy resource for renewable energy platforms that enables them to meet sustainable technical requirements. The TES technique is divided into three categories; sensible TES, latent-heat TES, and thermo-chemical TES. The best of

This paper proposes a method for project selection based on stochastic dominance (SD) and fuzzy theory. Using fuzzy theory and stochastic dominance, the method is tested using data from real-world projects. The findings show the importance of the proposed methodology improved existing methods in two ways: (1) This research alleviates the subjective bias in risk assessment in estimating the expected

The probabilistic linguistic term set (PLTS) is an effective tool for describing linguistic value in the process of decision-making. However, due to its complex structure, there remains numerous defects in utilizing the PLTS information. Therefore, by connecting the PLTS with discrete probability distribution, this study investigates a novel probability distribution-based processing model to process

Failure mode and effect analysis (FMEA) is a forceful way for preventing the occurrence of possible damages before the process of management activities, which is widely utilized in various fields. However, experts’ bounded rationality, imprecise risk assessments and unobjective risk factors’ weights are prominent challenges existed in the conventional FMEA model. Thus, to better meet the challenges

This study considers the tracking control problem of the nonstrict-feedback nonlinear system with unknown backlash-like hysteresis, and a finite-time adaptive fuzzy control scheme is developed to address this problem. More precisely, the fuzzy systems are employed to approximate the unknown nonlinearities, and the design difficulties caused by the nonlower triangular structure are also overcome by

This paper investigates the problems of the observer-based fault estimation (FE) and fault-tolerant control (FTC) for nonlinear systems subjected to external disturbances by the T–S fuzzy model method with local nonlinear models. Firstly, an FE strategy is proposed based on the unknown input observer technology, where the local nonlinear terms can be decoupled from the FE error system for relaxing

This paper addresses the observer-based finite-time \(H_{\infty }\) control problem for Takagi–Sugeno (T–S) fuzzy semi-Markovian jump systems with mode-dependent fast-varying delays, actuator faults, parameter uncertainties and partly uncertain transition rates. Firstly, a more general actuator fault model is considered and the fuzzy reliable observer-based control scheme is designed. By applying the

In this paper, the lower and upper admissible mean, downside semi-covariances and downside semi-variances of assets are first defined. Considering the transaction costs, borrowing constraints and threshold constraints, we present a new weighted lower and upper admissible mean downside semi-variance portfolio selection model which is a convex quadratic programming problem. The improved pivoting algorithm

In this paper, we discuss a portfolio selection problem based on fuzzy data envelopment analysis cross-efficiency evaluation wherein both undesirable fuzzy inputs and outputs have been considered. We first propose a data envelopment analysis cross-efficiency model, in which both undesirable inputs and outputs are considered. Furthermore, considering the imprecision of the data, we extend the crisp

The q-rung orthopair uncertain linguistic set, which combines the q-rung orthopair fuzzy set and uncertain linguistic variable, can simultaneously represent the quantitative and qualitative information given by experts. In the procedure of addressing q-rung orthopair uncertain linguistic information, to eliminate extreme evaluation values for arguments and capture the multilayer heterogeneous relationship

In this paper, the path following control scheme of a fully actuated marine surface vessel under the condition of model uncertainties and unmeasurable states is developed. Firstly, an adaptive fuzzy state observer is designed to estimate the unmeasurable states of the vessel. To guarantee the robustness of the system, a kind of sliding mode intermediate variable with the improved approaching law is

The early diagnosis of Parkinson’s disease (PD) is important to reduce deaths due to this disease. Images of patients with PD obtained through magnetic resonance imaging (MRI) show gray matter, white matter, and cerebrospinal fluids; images of these components provide physicians with important information to determine the severity of the disease. Therefore, methods for segmenting these regions in images

The ultra-low resolution underwater terrain maps of the Arctic region reduce the localization and navigation accuracy of the underwater vehicle relying on terrain-aided navigation. In this paper, we study the navigation ability of Autonomous Underwater Vehicles (AUVs) under the ultralow-resolution terrain map. Firstly, the low-resolution map is transformed into a continuous map by bilinear interpolation

In recent years, the important role of sustainable development is highlighted in the literature; therefore, a company to make a sustainable business success needs to be well focused on social, environmental, economic, and technological characteristics of the business to address risks and capture value properly. The process of Sustainability Enterprise Risk Management (SERM) is typically implemented

The failure mode and effect analysis (FMEA) is a prospective engineering technique which can prevent risk in advance by identifying, assessing potential failure modes and their effects. However, the traditional FMEA approach possesses the limitations in improperly identifying risk factor weights, inefficiently calculating risk priority number and improperly evaluating failure modes in practice. This

Master production schedules (MPS) and Material requirements planning (MRP) are two important planning modules in the production planning system. In this paper, a new fuzzy credibility-based double-sided chance-constrained programming (FCDCP) model is proposed for the capacitated MPS/MRP integrated programming problem under complex uncertainty. The proposed model supports simultaneous lot sizing and

The aggregation of multi-source information can be realized by means of the ordered representation of polygonal fuzzy sets and their linear operations, especially the ordered representation plays an important role in multi-attribute decision analysis. An intuitionistic fuzzy set is not only a generalization of traditional fuzzy set, but also can overcome some defects of fuzzy phenomena described by

In this study, a mobile overground gait-training system (OGTS) was designed, and a prototype was manufactured to determine the feasibility and control stability of the designed system. Approval was received from the Institutional Review Board of Fu Jen Catholic University for conducting experimental testing on healthy individuals with the designed mobile OGTS. The designed system comprises a mobile

With microgrids gaining more popularity in effectively meeting the increasing power demand, the operational challenges keep on increasing hand-in-hand. Especially when the microgrid work in islanded mode, it has to supply its area loads and maintain the system parameters as per the standards. This requires systematic coordination among the distributed generators so that the real and reactive power

The distributed hybrid flowshop scheduling (DHFS) problem is a common scheduling problem that has been researched in both academic and industrial fields during recent years. The uncertainty levels in realistic applications are generally too high to be represented by a deterministic value or a triangular fuzzy number (TFN) value. Considering the DHFS problem with type-2 fuzzy processing time and setup

The adaptive stabilization issue for complicated nonlinear systems is investigated in this work. The discussed problem is comparatively challenging since the system owns non-triangular structure and involves unknown coefficients, unknown parameters, and complex dynamics. By proposing an improved fuzzy-based adaptive control approach, a new stable control law is designed to make semi-globally uniformly

This paper proposes a Delta-Modulator (\(\Delta \)-M)-based quantised state feedback controller for Takagi–Sugeno (T–S) fuzzy networked systems. The \(\Delta \)-M (a single bit quantiser) essentially belongs to one class of sliding mode quantisers (SMQs) and offers various advantages which include lower design complexity, lower cost and less noisy. For a prescribed quantisation error, the gains of

This paper is concerned with a novel control technique for automated lane keeping of a vehicle, which takes advantage of an exact fuzzy modelling of bounded parametric uncertainties—both constant and varying—for a convex treatment of local characteristic polynomials, put together via parameter-dependent Lyapunov analysis. It is shown that the specificity of the proposed technique enlarges the feasibility

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s40815-021-01067-0.

The classification of electroencephalogram (EEG) signals plays a key role in detecting brain activities. Fuzzy methods are widely applied in decision-making problems because they are effective tools for handling imprecise and vague data. This paper proposes a modified algorithm to calculate the center of gravity of generalized trapezoidal fuzzy numbers. Accordingly, we introduce a new similarity measure

In this paper, a novel maximum fuzzy correntropy Kalman filter (MFC-KF) algorithm is proposed to solve the problem that the effect of different samples on state estimation is uncertain in common correntropy. In the proposed algorithm, a new optimization criterion—the maximum fuzzy correntropy criterion with fuzzy correntropy based on fuzzy information theory—is used to optimize the Kalman filter, by

In this paper, a novel Type–2T-S fuzzy control scheme is proposed for a class of nonlinear systems with uncertain parameters. In this scheme, the fuzzy controller with biological characteristics is firstly constructed by taking the niche equality index function as the consequent of type-2 fuzzy rules, which combines the adaptive characteristics of biological evolution with type-2 fuzzy control. Then

Many works provide intrinsic rewards to deal with sparse rewards in reinforcement learning. Due to the non-stationarity of multi-agent systems, it is impracticable to apply existing methods to multi-agent reinforcement learning directly. In this paper, a fuzzy curiosity-driven mechanism is proposed for multi-agent reinforcement learning, by which agents can explore more efficiently in a scenario with

In image pre-processing, edge detection is a non-trivial task. Sometimes, images are affected by vagueness so that the edges of objects are difficult to distinguish. Hence, the usual edge-detecting operators can give unreliable results, thus necessitating the use of fuzzy procedures. In literature, Chaira and Ray approach is a popular technique for fuzzy edge detection in which fuzzy divergence formulation

The practical applications of engineering designs are often concerned by the presence of interrelated decisions. Naturally, these decisions are induced by solving many conflicting and incommensurable objectives. To deal with the interlinked decisions as well as the paradox natures among objectives, this paper presents an integrated approach based on dynamic programming approach (DPA) and intuitionistic

This paper proposes a new approach by applying Kruskal’s algorithm on undirected road graph under intuitionistic fuzzy environment. The new hybrid algorithm consists of two phases. The first one is to determine fuzzy weights of undirected graph by intuitionistic fuzzy multi-criteria decision-making method with Hamacher aggregation operator. The edges between vertices are weighted by the determined

Predicting or deducing war outcomes is the basis of military strategy formulation. Considering the high information uncertainty and complex influencing factors of modern wars, we applied a fuzzy influence diagram (FID) to realize the macroscopic prediction of war outcomes. In particular, we identified multidimensional factors that influence war outcomes in a relatively comprehensive and practical manner

Large-scale group decision making (LSGDM), which involves a large number of decision makers, has become a hot topic in the field of decision making. To address LSGDM problems with hesitant information, in this paper, a distance-based LSGDM method is proposed by considering group influence. In the method, a generalized distance measure between two pieces of hesitant information is designed to overcome

Membership function estimation is one of the less explored, albeit important, areas in fuzzy sets. This paper aims to define a new family of fuzzy sets called general continuous linguistic variables (GCLV), which represents a linguistic variable rather than a set of linguistic values. We refer to it as the principle of representation of linguistic variables. They are based on the well-known sigmoidal

One of the essential features of project management is the novelty of work and uncertainty in the estimation of activities which will, in turn, result in uncertainty in project scheduling. In this study, a multi-objective multi-skill project scheduling problem is examined in terms of fuzzy time with two main objectives including: minimizing the project's makespan and minimizing the total cost of labor

This article settles the issue of adaptive fixed-time control of uncertain nonlinear quantized systems. Different from the traditional study about fixed-time control for uncertain nonlinear systems, quantitative control issue is considered in this paper, and the nonlinear term can be unknown. The new adaptive control tactic of fixed-time tracking control is proposed via fuzzy logic systems approaching

This paper investigates asynchronous mixed passivity and \(H\infty \) filter design for fuzzy jump systems with time-varying delays. Firstly, new time delay-dependent stability conditions for the fuzzy Markovian jump systems are obtained by constructing a mode-dependent Lyapunov functional and introducing free weight matrices technique. Secondly, the asynchronous filter is designed via linear matrix

This paper considers one kind of nonlinear uncertain system which is disturbed by general external disturbance generated by an exogenous system. The partial known information of the external disturbance will be efficiently applied to construct an adaptive disturbance observer (DO) for estimating the unknown external disturbance. The nonlinear uncertainties are also considered in this paper, and the

This paper first discusses the establishment of multi-attribute decision problem model and the determination of weights. Then this paper points out a situation that the method of updating weights using the fixed weight vector cannot effectively describe the weights’ variation when the state vector is continuously changing. To get rid of this problem, an iterative weight updating method is proposed

Gaussian mixture model (GMM) is a well-known model-based approach for data clustering. However, when the data samples are insufficient, the classical GMM-based clustering algorithms are not effective anymore. Referring to the idea of transfer clustering methods, this paper proposes a general transfer GMM-based clustering framework, which employs the important knowledge extracted from some known source

This paper investigates the switching control issue for uncertain vehicle suspension systems, meanwhile, actuator failure is taken into account. Takagi-Sugeno (T-S) fuzzy model is utilized to describe the considered uncertain suspension systems. Consider that most existing analysis results related to suspension systems are independent of membership functions, which may cause conservatism, thus, a novel