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In this paper, a new two-dimensional gaussian hierarchical priority fuzzy system (TGHPFS) is proposed to handle interval-valued data. TGHPFS first performs hierarchical clustering of the average value of interval-valued data in each dimension to generate two-dimensional gaussian membership functions of two-level rules. The two levels of rules are associated by calculating the activation strength of the second-level rules to the first-level rules and setting the connection threshold. The regularized least squares method is used to optimize the consequents of the second-level rules. The two-dimensional gaussian membership function designed in this paper is used to model the antecedents of interval-valued data, solving the correlation problem between the left and right values of interval-valued data. The effectiveness of TGHPFS is validated using real-world datasets, and the proposed method is compared with other latest methods to show the superiority of TGHPFS. [ABSTRACT FROM AUTHOR]

In the paper, a class of unknown fractional-order nonlinear systems suffering from actuator faults are investigated. Meanwhile, an adaptive finite-time sliding mode control (SMC) approach based on approximation principle of fuzzy logic system (FLS) and backstepping layout is proposed. It is well known that the standard backstepping control has inherent computational complexity. Therefore, a type of fractional-order command filter (CF) is introduced to overcome such a shortcoming, that is, by means of fractional-order CF, the virtual input signal and its fractional derivative can be estimated properly as anticipated. Fractional-order sliding mode surfaces are constructed to diminish the filtering errors such that more better performance is guaranteed. Besides, compared to the conventional backstepping control, the CF-based fuzzy backstepping SMC approach presented in the paper not only shows the superior robustness, but also facilitates to accomplish the desired tracking control objective in finite time. The finite-time stability analysis is established on the basis of fractional-order Lyapunov method. Finally, the effectiveness of the proposed methodology is identified by numerical simulations. [ABSTRACT FROM AUTHOR]

Ensemble learning is a widely used methodology to build powerful predictors from multiple individual weaker ones. However, the vast majority of ensemble learning models are designed for offline application scenarios, the use of evolving fuzzy systems in ensemble learning for online learning from data streams has not been sufficiently explored, yet. In this paper, a novel self-adaptive fuzzy learning ensemble system is introduced for data stream prediction. The proposed ensemble system employs the very sparse random projection technique to compress the consequent parts of the learned fuzzy rules by individual base models to a more compressed form, thereby reducing redundant information and improving computational efficiency. To improve the overall prediction performance, a dynamical base model pruning scheme is introduced to the proposed ensemble system together with a novel inferencing scheme, such that less accurate base models will be removed from the ensemble structure at each learning cycle automatically and only these more accurate ones will be involved in joint decision-making. Numerical examples based on a wide range of benchmark datasets demonstrate the stronger prediction performance of the proposed ensemble system over the state-of-the-art alternatives. [ABSTRACT FROM AUTHOR]

• A dynamic trade-off between conservatism and complexity is established via a online evaluator. • More efficient fuzzy stabilization conditions are developed in this paper. • The recent multiple-sums-based method is improved evidently in the study. This paper is concerned with the problem of developing more efficient stabilization conditions for discrete-time Takagi-Sugeno fuzzy systems, i.e., not only reducing the conservatism but also (at the same time) alleviating the complexity of fuzzy control synthesis. Firstly, under the framework of traditional fuzzy stabilization, the recent multiple-sums-based method reported in the literature is improved by removing all the redundant variables, and thus the same feasible stabilization range can be obtained at the cost of more economical computational burden. Secondly, in order to further enhance the control efficiency, a dynamic trade-off between conservatism and complexity is established by proposing a new online evaluator of the updated system variation information across two adjacent sampling instants, and thus the so-called resilient stabilization is developed for reducing the conservatism without increasing or even alleviating the complexity. Moreover, all the determined matrices at the farthest sampling instant can be entirely removed without introducing any conservatism due to the proposed substitution technique. Finally, the superiority and effectiveness of our proposed methods are illustrated by a set of simulation comparisons with relevant research results reported in recent literature. [ABSTRACT FROM AUTHOR]

There have been several studies in the literature that looked at bipolar fuzzy relation equations using three compositions, max-min, max-product, and max-Lukasiewicz. This paper addresses an optimization problem involving a general class of bipolar fuzzy relation equations. A point to be noted is that the constraints are formed as a system of bipolar fuzzy equations, in which the fuzzy compositions are defined by an arbitrary continuous t-norm. The paper shows that a feasible solution set consists of a finite number of closed convex cells whose lower and upper bounds are determined by corner (extreme) points. In addition, it will be illustrated that, in contrast to the (ordinary) fuzzy relational equations, corner points may neither be the minimum (maximum) nor minimal (maximal) solutions to the feasible region. The generality of the problem leads us to divide the constraints into four classes (each with a different structure) and then investigate the specific characteristics of the equations in each class. Additionally, to evaluate the feasibility of the problem in general, some necessary and sufficient conditions are presented. Due to the high computational complexity of the complete solution of FREs, six rules are introduced to reduce the size of the original problem in such a way that the effort to solve the problem is significantly decreased, followed by an algorithm for solving the current optimization problems. Accordingly, it is shown that the problem has a finite number of local optimal solutions and a global optimal solution can always be obtained by choosing a point having the minimum objective value compared to all the local optimal solutions. In the final section, we will examine an example to demonstrate how the algorithm works. • A generalized bipolar system of fuzzy relation equations programming problem has been introduced which is defined with linear objective function, bipolar fuzzy constraints and an arbitrary continuous strict t-norm. • It is proved that the feasible solution set is formed as a union of the finite number of closed convex cells whose lower and upper bounds are determined by corner (extreme) points that may be neither the minimum (maximum) nor minimal (maximal) solutions. • The constraints of the problem are categorized into four different classes, and the special characteristics of the equations in each class are investigated. • Some necessary and sufficient conditions are presented to conceptualize the feasibility of the problem. • Six rules are introduced with the aim of simplifying the original problem. [ABSTRACT FROM AUTHOR]

Evolving fuzzy systems (EFSs) are a type of adaptive fuzzy rule-based systems which can self-adapt both their structures and parameters simultaneously. However, the existing EFSs suffer from two drawbacks: 1) classical EFSs usually use all input features to model systems, resulting in lengthy fuzzy rules; 2) some redundant information in fuzzy rules may hinder high generalization. To address these two issues, a promising method is proposed in this paper by combining very sparse random projection (VSRP) with a class of EFSs based-on data clouds, called VSRP-AnYa-EFS. The proposed method introduces: 1) a random sparse-Bernoulli (RSB) matrix based-on VSRP is utilized to compress the lengthy antecedent part into a tighter form, triggering a feature-reduction mechanism. By employing VSRP in RSB matrix, some redundant information in fuzzy rules can be filtered; 2) Local learning is used for consequent parameter optimization to suit decoupled behavior of rules after redundant information between rules is deleted. By adopting VSRP and local learning, the proposed VSRP-AnYa-EFS owns a compact structure and fast learning speed. Numerical examples presented in this paper demonstrate that the proposed method can significantly reduce training time from hours to minutes while the accuracy can be improved up to 5%. [ABSTRACT FROM AUTHOR]

This paper introduces an admissibilization condition for singular interval-valued fuzzy systems with a dynamic output-feedback controller using a linear matrix inequality approach. The derivation of the admissibility criterion (satisfying regularity, non-impulsiveness and stability) for the closed-loop system of the singular interval-valued fuzzy systems using the dynamic output-feedback controller is concerned. Here, the derived criterion is represented as the parameterized matrix inequalities depending on the membership functions of the system and the controller. To relax the derived parameterized matrix inequalities, this paper proposes a relaxation lemma based on the properties of the membership functions and their relations. By using this lemma, the parameterized matrix inequalities are converted into the matrix inequalities independent of the membership functions but not convex. Therefore, by introducing the structures of the variables and the congruent transformation matrix, a sufficient condition for the admissibility criterion is successfully given in terms of strict linear matrix inequalities. Two numerical examples are given to show the effectiveness of the proposed control. [ABSTRACT FROM AUTHOR]

Non-singleton fuzzification is used in rule-based fuzzy systems when the measurements that activate them are imperfect or uncertain or when their inputs are words. It models such measurements or words as fuzzy numbers or more general fuzzy sets so that, regardless of the cause of a measurement's or word's imperfections or uncertainties, they are treated within the framework of fuzzy sets and systems. Since 2011, there has been a resurgence of interest in both type-1 and interval type-2 non-singleton fuzzy systems. This paper removes a computational bottleneck associated with computing the firing level or firing interval for such fuzzy systems, by providing closed-form formulas for them when the involved fuzzy sets are trapezoidal or triangular, which are widely used fuzzy sets. This is done for both the minimum and product t-norms. It is also demonstrated that a non-singleton fuzzy system that uses the product t-norm has the potential to outperform a non-singleton fuzzy system that uses the minimum t-norm. The results in this paper greatly simplify non-singleton fuzzy systems, which should make them much more popular. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of reliable fuzzy prognosis of decentralized fuzzy discrete event systems (FDESs) in face of possible unavailability of some local agents. Firstly, the notion of k -reliable fuzzy coprognosability is formalized for a decentralized FDES with m local agents, which requires that a k -reliable fuzzy coprognosable FDES can predict the fault to a certain degree in the manner of fuzziness even when m - k local agents are unavailable, where 1 ⩽ k ⩽ m. Then, a reliable fuzzy copredictor is constructed to perform the reliable fuzzy prognosis of decentralized FDESs, and a necessary and sufficient condition of the k -reliable fuzzy coprognosability is presented. In particular, an algorithm based on reliable fuzzy copredictor is proposed to verify the k -reliable fuzzy coprognosability of FDESs. [ABSTRACT FROM AUTHOR]

• A new cost function is exploited. • The guaranteed cost control problem for T-S fuzzy NCSs with multiple quantizations is investigated. • An additional nonlinear term is employed in the control scheme. This paper researches the robust guaranteed cost (GC) control problem for a class of networked Takagi–Sugeno (T-S) fuzzy systems with local nonlinear segments and multiple quantizations, where the multiple quantizations mean that both the control input and the state are quantized through diverse dynamic quantizers. Three aspects are taken into account to perform the present study in depth. Firstly, the multiple quantizations case is investigated. Second, a novel cost function is exploited to perform the GC analysis issue, which consists of the quantized state and quantized control input. At last, a new control scheme is developed to fulfill the design of GC controller, where an additional nonlinear term is also employed besides the quantized state. By introducing auxiliary scalars and matrices, new conditions with regard to designing the dynamic quantizers and robust GC controllers are derived, which are expressed as a set of linear matrix inequalities (LMIs). Finally, a practical example is provided to verify the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

Overlap functions, as a class of not necessarily associative novel aggregation functions, have played an important role in the relevant theory and applications involving fuzzy sets and systems. Recently, extension study has become a hot topic in the theoretical research of overlap functions, especially for the so-called quasi-overlap functions. In this paper, we investigate the set-based extended quasi-overlap functions, which are one kind of set-based extended aggregation functions proposed by Mesiar et al. lately. Specifically, after introducing the definition of set-based extended quasi-overlap functions, we study some of their elementary properties. Furthermore, we give two equivalent characterizations of set-based extended quasi-overlap functions by means of mappings from the set composed by all finite subsets of unit closed interval with no repetition of elements to unit closed interval and 0,1-aggregation functions recently proposed by the author, respectively. In particular, it is shown that the family of set-based extended quasi-overlap functions is closed to convex combination and that the repetition of values in the input vectors to be aggregated and adding any value between its minimum and maximum values have no effect on the output value under the aggregation of set-based extended quasi-overlap functions. The obtained results afford theoretical basis for more potential applications of quasi-overlap functions in real problems, especially when there are more than two classes or objects in fuzzy context or fuzzy sets for which we need to measure the degree of overlapping or generate overlap indices. [ABSTRACT FROM AUTHOR]

The design of switching functions is extremely important in the design of sliding mode controllers in that it closely relates to the solvability of the sliding mode control problems. This paper is concerned with the design problems of switching functions and fuzzy sliding mode controllers for T-S fuzzy descriptor systems. First, a generalized regular form of T-S fuzzy descriptor systems is defined. Then the design method of the linear switching function-based fuzzy sliding mode controller is developed and the result obtained indicates a relationship between the design parameter in the switching function and the gain matrix in the controller of T-S fuzzy systems. From this relationship, a new framework of the switching function design for T-S fuzzy descriptor systems is formulated. Moreover, the design method of the fuzzy switching function-based sliding mode controllers is proposed to exemplify the proposed framework. It is shown that the proposed fuzzy switching function-based method is less conservative than the linear switching function-based method from the viewpoint of the solvability of the sliding mode control problems. Finally, the results obtained are verified through four illustrative examples. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of mixed actuator failures for discrete-time interval type-2 (IT-2) fuzzy systems with time-varying delays and external disturbances. Applying the non-parallel compensation strategy, the IT-2 fuzzy controller is designed. Considering the nonlinear fault factors of actuator, the linear fault model is expanded, which is more general than the conventional mixed actuator failure model. The information of membership function is introduced by staircase function approximation. Based on Lyapunov stability theory and a series of matrix inequality scaling techniques, a membership function dependent stability criterion with H ∞ performance is obtained. Then, the effectiveness of the proposed conclusion is verified by two simulation examples. [ABSTRACT FROM AUTHOR]

Currently, labelling a large number of images is still a very challenging task. To tackle the problem of unlabelled data, unsupervised learning has been proposed. Among many unsupervised learning algorithms, K-means is the most popular algorithm. However, in a low-dimensional space, fuzzy c-means, which is more robust and less sensitive to initialization, has several advantages over K-means clustering. On the other hand, stacked convolutional pooling structures and manifold representation play pivotal roles in image clustering. In this paper, we propose an unsupervised multilayer fuzzy neural network for image clustering that unifies fuzzy systems, multilayer convolutional structures and manifold representation. The main contributions are as follows. First, we extend fuzzy systems to unsupervised tasks by introducing manifold representation, which expands the applications of fuzzy systems. Next, we propose the idea of using only a small number of attributes to compute firing strengths. This is implemented to prevent the firing strengths from falling to zero. Finally, randomly generated convolutional weights are used to extract features, which is a good choice for data without labels. It is demonstrated on a wide range of image datasets that the proposed approach is competitive with existing fuzzy and nonfuzzy clustering algorithms. [ABSTRACT FROM AUTHOR]

Formation control of multi-agent systems has a wide range of applications, but it has not been paid much attention in Takagi–Sugeno (T-S) fuzzy systems. In this paper, an event-triggered sampling strategy is designed for the formation control of T-S fuzzy systems, which allows the fuzzy controller and the fuzzy model not to share the same membership functions. The control strategy reduces the transmission frequency of sampling states by setting event conditions for information transmission. By means of Lyapunov stability theory, a set of formation conditions based on LMI is established. Less conservative and more efficient conditions are also obtained based on the properties of membership functions. Numerical examples show the validity of the proposed formation conditions, and the conservatism of the two criteria is compared. [ABSTRACT FROM AUTHOR]

Currently, labelling a large number of images is still a very challenging task. To tackle the problem of unlabelled data, unsupervised learning has been proposed. Among many unsupervised learning algorithms, K-means is the most popular algorithm. However, in a low-dimensional space, fuzzy c-means, which is more robust and less sensitive to initialization, has several advantages over K-means clustering. On the other hand, stacked convolutional pooling structures and manifold representation play pivotal roles in image clustering. In this paper, we propose an unsupervised multilayer fuzzy neural network for image clustering that unifies fuzzy systems, multilayer convolutional structures and manifold representation. The main contributions are as follows. First, we extend fuzzy systems to unsupervised tasks by introducing manifold representation, which expands the applications of fuzzy systems. Next, we propose the idea of using only a small number of attributes to compute firing strengths. This is implemented to prevent the firing strengths from falling to zero. Finally, randomly generated convolutional weights are used to extract features, which is a good choice for data without labels. It is demonstrated on a wide range of image datasets that the proposed approach is competitive with existing fuzzy and nonfuzzy clustering algorithms. [ABSTRACT FROM AUTHOR]

Formation control of multi-agent systems has a wide range of applications, but it has not been paid much attention in Takagi–Sugeno (T-S) fuzzy systems. In this paper, an event-triggered sampling strategy is designed for the formation control of T-S fuzzy systems, which allows the fuzzy controller and the fuzzy model not to share the same membership functions. The control strategy reduces the transmission frequency of sampling states by setting event conditions for information transmission. By means of Lyapunov stability theory, a set of formation conditions based on LMI is established. Less conservative and more efficient conditions are also obtained based on the properties of membership functions. Numerical examples show the validity of the proposed formation conditions, and the conservatism of the two criteria is compared. [ABSTRACT FROM AUTHOR]

• The definition of dissipativity for 2-D positive systems is given for the first time. • Controllers are designed to guarantee the dissipativity of the closed-loop systems. • The minimum dwell time method and M-P inverse are utilized in this paper. • Dissipative characteristics of the feedback interconnected system are presented. This paper addresses the dissipativity analysis and controller design for a class of T-S fuzzy positive systems which are described by the Roesser model. First, sufficient conditions obtained by utilizing the minimum dwell time method and co-positive Lyapunov function approach are given to guarantee the considered system is 2-D (ζ , η)- α -dissipative. Then via the state feedback fuzzy controller and the static output feedback fuzzy controller, criteria are also presented under which the resulting closed-loop systems are positive and 2-D (ζ , η)- α -dissipative. Moreover, the explicit design schemes for the desired controller parameters are also presented in the form of linear programming. Finally, a simple illustrative example is provided to show the validity of the approach outlined in this article. [ABSTRACT FROM AUTHOR]

In this paper, the problem of exponential dissipation stability of T-S fuzzy system with state quantization is studied by using non-fragile sampled-data control. First, A Lyapunov-Krasovskii function containing all sampled-data and quantization information is constructed. Second, the better results can be obtained by using the integral inequality and the Newton Leibniz formula. In addition, a dissipative controller for non-fragile sampled-data is designed. Finally, The reasonable examples illustrate the significant improvement and value of this paper. [ABSTRACT FROM AUTHOR]

This paper concentrates on the H ∞ consensus filtering issue of a kind of nonlinear two-dimensional (2-D) systems over sensor networks, which are described by the Takagi-Sugeno fuzzy model. With consideration of the limited communication bandwidth and the malicious network attack behavior, a distributed event-triggered mechanism is considered for the sensor network under DoS attacks. Different from the implicit assumption of many previous results that detail information of the non-periodic DoS attacks is available, a switching-like distributed event-triggered mechanism is introduced under the 2-D framework, which could schedule the triggering strategy flexibly in the attack silent or active regions. By means of the switching system analysis method and the discontinuous Lyapunov functions, this paper aims to develop a proper distributed attack-resilient H ∞ consensus filter based on the information from the local sensor and its neighbors. A set of sufficient conditions are presented so that the estimation error system can reach exponential stability and meet an expected H ∞ performance. Finally, a numerical example is given to demonstrate feasibility of the filter designing method. • The H ∞ consensus filtering issue for a kind of nonlinear 2-D systems over sensor networks is concerned. • A switching-like distributed event-triggered mechanism under the 2-D framework is considered with DoS attacks. • Sufficient conditions are presented to ensure exponential stability with a prescribed H ∞ performance. • An attack-resilient distributed H ∞ consensus filter is developed based on the local sensor and its neighbors. [ABSTRACT FROM AUTHOR]

• Bibliometric techniques identify the productivity and influence on research. • Fuzzy systems are an area of study with potential for further research. • Zadeh is known as the most influential author in fuzzy research in North American. • The United States is the most productive and influential country in fuzzy research. The objective of this paper is to apply bibliometric techniques to analyze the evolution of fuzzy systems research (FSR) in the United States of America and Canada. The aim is to visualize the progression of FSR since Zadeh's first publication of fuzzy sets and the regional impact that these ideas have in the scientific community. This work obtains data retrieved from the Web of Science (WoS) scientific database. Using a hybrid methodology designed to maximize the obtention of representative results, we analyze 20,317 documents published between 1965 and 2020. The main findings show that Zadeh is the most influential author and that Pedrycz is the most productive author at FSR. Journals such as Fuzzy Sets and Systems (FSS) and IEEE Transactions on Fuzzy Systems (TFS) publish most of the FSR articles. The results show a greater contribution by the United States of America compared to Canada in FSR. This analysis contributes to visualization and evolution in the field of study of FSR in diverse regions. [ABSTRACT FROM AUTHOR]

Nowadays, computer science has the ability of assisting experts in different application areas. Recently there has been increasing attention in the health area, and there exist different approaches based on artificial intelligence that have been proposed in the diagnosis of several kinds of diseases. In particular, fuzzy systems have been successfully used as Diagnosis Systems, in this way helping doctors to realize a faster and more accurate diagnosis. However, with the emergence of Type-2 Fuzzy Systems, there have been important improvements in handling the uncertainty with respect to traditional Fuzzy Systems (now called Type-1 Fuzzy Systems) in different kinds of problems. In the present paper, a new approach to Fuzzy Diagnosis based on Type-2 Fuzzy Systems is proposed and compared with respect to Type-1 Fuzzy Systems on a set of diagnosis problems, in order to evaluate the relevance of the uncertainty handling in this kind of problems. On the other hand, the paper is also aiming at observing the accuracy behavior in Fuzzy Diagnosis Systems by changing the uncertainty level in the models. Finally, a comparison of Interval Type-2 Fuzzy Systems with respect to General Type-2 Fuzzy Systems for a set of diagnosis problems is presented. [ABSTRACT FROM AUTHOR]

The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. [ABSTRACT FROM AUTHOR]

This paper addresses the energy-to-peak bumpless transfer control for switched interval type-2 fuzzy delayed systems using combined switching. The non-increasing condition of the Lyapunov functional at the switching instants which is commonly required in the existing results is removed by adopting a generalized combined switching strategy. A novel bumpless transfer performance associated with combined switching is established. By using an improved dwell-time-dependent Lyapunov–Krasovskii functional and the adopted switching strategy, sufficient conditions for asymptotic stability with energy-to-peak bumpless transfer performance are obtained. Finally, a tunnel diode circuit model is exploited to illustrate the applicability and effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

This paper presents the memory-based Takagi–Sugeno (T-S) fuzzy state feedback controller (SFC) for switched nonlinear systems based on the admissible edge-dependent average dwell time (AEDADT) rule. Different from the existing studies, the fuzzy dependent exponential Lyapunov–Krasovskii functional (LKF) is introduced for switched nonlinear systems, which include the notion of the derivative of membership functions based on the switching idea. Besides that, when compared to the traditional SFC framework, a time-varying delay is involved with the AEDADT switching rule in control gain matrices that are considered for the first time to tackle the stabilization problem. Further, the AEDADT switching property is considered for proposed nonlinear systems, and it contributes to getting more information about previous modes. Based on the proposed membership function dependent exponential LKF, new sufficient conditions are carried out to stabilize the proposed nonlinear systems in terms of the linear matrix inequalities (LMIs) technique. To support the applicability and efficacy of the proposed controller scheme, the tunnel diode circuit system model is illustrated. Also, the feasibility of the obtained results is verified by the numerical example. [ABSTRACT FROM AUTHOR]

During the last two decades, evolving fuzzy systems (EFSs) have attracted more and more attention owing to their capacity to self-adapt both system structures and parameters dynamically. Conventional EFSs optimize the consequent parameters using the recursive least square (RLS) alg orithm based on mean square error (MSE) criterion. It only works well in Gaussian noise or noise-free situations. In this paper, we further propose an alternative approach to update consequent parameters based on the novel least mean dual p-power (LMDP) error criterion. By contrast with the MSE criterion, the LMDP criterion has a stronger noise rejection ability on non-Gaussian noise environments. Moreover, the LMDP criterion extends the range of p value in conventional LMP criterion from positive integer to floating point for more fine-grained optimum solutions. Details in this work, the recursive solution of LMDP criterion is respectively derived from global learning and local learning to update consequent parameter and the theoretical stability analyses for both learning cases are proven in that the stability of our considered EFS is guaranteed. The experiments on various benchmark datasets and a real-world typhoon path prediction validate that the proposed method is more accurate and owns better generalization performance under multiple noise conditions. [ABSTRACT FROM AUTHOR]

This paper presents an online-learning ensemble framework for nonstationary time series prediction. Optimal granular fuzzy rule-based models with different objective functions and constraints are evolved from data streams. Evolving optimal granular systems (eOGS) consider multiobjective optimization, the specificity of information, model compactness, and variability and coverage of the data within the process of modeling data streams. Forecasts of individual base eOGS models are combined using averaging aggregation functions: ordered weighted averaging (OWA), weighted arithmetic mean, median, and linear non-inclusive centered OWA. Some aggregation functions use specific weights for the relevance of the base models and exclude extreme values and outliers. The weights of other aggregation functions are adapted over time based on a quadratic programming problem and the data within a sliding window. This paper investigates whether an online-learning ensemble can outperform individual eOGS models, and which aggregation function provides the most accurate forecasts. Real multivariate weather time series, particularly time series of daily mean temperature, air humidity, and wind speed from different weather stations, such as Paris–Orly, Frankfurt–Main, Reykjavik, and Oslo–Blindern, are used for evaluation. The results show that ensemble schemes outperform individual models. The proposed linear non-inclusive centered OWA function provided the most accurate numerical predictions. [ABSTRACT FROM AUTHOR]

In this paper, optimization of a linear objective function with fuzzy relational inequality constraints is investigated In doing so, the feasible region is formed by the intersection of two inequality fuzzy systems and Dubois-Prade family of t-norms which are considered as fuzzy composition. The most well-known continuous t-norms are Archimedean such as Frank, Yager, Hamacher, Sugeno-Weber and Schweizer-Sklar family. An interesting family of t-norms that is not Archimedean has been introduced by Dubois and Prade. In this paper, the resolution of the feasible region of the problem is initialy investigated when it is defined with max-Duboise-Prade composition. A necessary and sufficient condition along with three other necessary conditions are derived for determining the feasibility. of the problem. Moreover, two procedures have also been presented with the aim of simplifying the current linear problems. A method is proposed to generate random feasible max-Dubois-Prade fuzzy relational inequalities and an algorithm is accordingly presented to solve the problem. Finally, an example is described to illustrate this algorithm. [ABSTRACT FROM AUTHOR]

Analysing the interpretability of a fuzzy system (either hierarchical or not) involves consideration of its semantic properties and evaluation of its structural complexity. The present paper concentrates on the semantic aspects of interpretability in hierarchical fuzzy systems. Complexity reduction is also considered, but from the perspective of its interaction with semantic preservation. In that sense, the paper shows that only the use of intermediate variables with meaning (interpretable variables) will transform the complexity reduction into a real improvement in interpretability. The paper formalises this idea by introducing the concept of semantically decouplable hierarchies. Under the assumption of semantic decouplability, it is shown that the interpretability of the overall hierarchical system can be directly obtained from that of its subsystems. Consequently, the paper defines and measures the interpretability of a semantically decouplable hierarchical fuzzy system as the aggregation of the interpretability of its subsystems. Finally, several options will be considered for this aggregation process. [ABSTRACT FROM AUTHOR]

This paper investigates the problems of state and static output feedback control for a class of singular Takagi–Sugeno fuzzy systems with time-varying delay. Two kinds of controllers are considered: one is the proportional plus derivative state feedback controller, the other is the proportional plus derivative static output feedback controller. The augmented Lyapunov–Krasovskii functional is chosen to analyze the stabilization of the considered system and the sufficient conditions are proposed based on the Lyapunov stability theory. In order to solve the controller gains, two iterative algorithms are built. Two persuasive examples are given to show the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

Multi-attribute decision making (MADM) plays an important role in decision analysis. Based on a new fuzzy rough set theory (FRS), this paper presents a multi-attribute prediction analysis model in fuzzy information systems (FISs). In this prediction model, the proposed FRS is based on an attribute-oriented fuzzy similarity relation; the cosine deviation of a candidate to optimistic decision and pessimistic decision is proposed to construct a trend function, for further predicting the development trend of the candidate. It deals with MADM problems from the perspective of multi-attribute predictive analysis, and is a valuable continuation of fuzzy decision making research. To evaluate the validity and feasibility of the proposed prediction model, experiments with real data sets and comparative experimental analysis are conducted. The experimental results prove that the prediction performance of the proposed model is well-pleasing in terms of both prediction accuracy and AUC results. [ABSTRACT FROM AUTHOR]

The double-input-rule-modules stacked deep fuzzy method (DIRM-DFM) has attracted much attention because of its interpretability and prediction accuracy. However, when confronted with high-dimensional data and large numbers of fuzzy rules, the original DIRM-DFM has three limitations. First, the identity mapping that appears in the depth layer causes degradation of the prediction performance. Second, redundant fuzzy rules increase the complexity of the structure. Third, the overfitting phenomenon in the parameter-learning process imposes restrictions on the generalization ability of the system. Therefore, achieving structural simplification and high-accuracy prediction remains challenging. In this paper, a compression and regularized optimization scheme for the modules stacked residual deep fuzzy system (CDIRM-RDFS) is presented to address the above-mentioned limitations. To overcome the first limitation, a residual approximation mechanism was developed to approximate the actual output layer by layer. To obtain more compact fuzzy modules, singular value decomposition was conducted to eliminate the unimportant fuzzy rules, thus surmounting the second limitation. Finally, to prevent overfitting, regularization terms combining the l 1 norm with the l 2 norm were added to the loss function to penalize the parameters in the learning process and thereby solve the third problem. The performance of the proposed CDIRM-RDFS was validated using various types of datasets, including seasonal and non-seasonal as well as cyclical and non-cyclical time series. Compared with some existing popular shallow and deep models, the experimental results indicate that the proposed methodology can simplify the system architecture while improving the accuracy, effectiveness, and robustness of the prediction model. [ABSTRACT FROM AUTHOR]

Nonstationary fuzzy inference systems (NFISs) model the variation in opinions of individual experts and expert groups. They have the capability similar to type-2 fuzzy systems in some regards with less computational costs. This paper presents a nonstationary fuzzy neural network (NFNN) model by combining NFISs and neural networks (NNs). The proposed model synthesizes the logical inference and language expression abilities of NFIS with the learning mechanism of NNs. Hence, it makes NNs more translatable and also achieves self-learning of fuzzy rules. Besides, a fuzzy c-means network (FCMnet) clustering and a modified conjugate gradient method with constrained Armijo-type rule (MCGA) are proposed to initialize and train NFNN, respectively. The proposed FCMnet first combines the fuzzy c-means (FCM) algorithm with NNs. Thus, it not only improves the performance of FCM, but also makes NNs more interpretable. For MCGA, we construct a specific conjugate coefficient to ensure the sufficient descent property and propose a constrained Armijo-type rule to search a suitable learning rate in each iteration. By adopting these two techniques, MCGA achieves a fast convergence speed. In addition, both weak and strong convergence results are proven rigorously. Experiments on 14 datasets are carried out to illustrate the competitive performance of our models. [ABSTRACT FROM AUTHOR]

An adaptive fuzzy output tracking control scheme is presented for a class of strict-feedback systems with an unknown control gain in this paper. An ideal controller that can achieve finite-time stabilization of the filtered tracking error system is approximated by an ideal fuzzy logic system. The actual fuzzy function is used as the controller to estimate the ideal fuzzy system via on-line adjustment of system parameters. A τ -observer (i.e., the control error observer) is designed to manage the unknown fuzzy control error. Unlike previous studies, new parameter adaptive laws are constructed in this study, and derivative calculations in the filtered tracking error system are not required. An adaptive direct fuzzy controller based on a τ -observer is thus proposed. The practical finite-time stabilization of the output tracking error and the boundedness of all states in the closed-loop system are also ensured. Finally, the effectiveness of the proposed control criterion is verified via two simulation examples. [ABSTRACT FROM AUTHOR]

Systems of fuzzy relation equations and inequalities in which an unknown fuzzy relation is on the one side of the equation or inequality are linear systems. They are the most studied ones, and a vast literature on linear systems focuses on finding solutions and solvability criteria for such systems. The situation is quite different with the so-called weakly linear systems, in which an unknown fuzzy relation is on both sides of the equation or inequality. Precisely, the scholars have only given the characterization of the set of exact solutions to such systems. This paper describes the set of fuzzy relations that solve weakly linear systems to a certain degree and provides ways to compute them. We pay special attention to developing the algorithms for computing fuzzy preorders and fuzzy equivalences that are solutions to some extent to weakly linear systems. We establish additional properties for the set of such approximate solutions over some particular types of complete residuated lattices. We demonstrate the advantage of this approach via many examples that arise from the problem of aggregation of fuzzy networks. [ABSTRACT FROM AUTHOR]

We develop an agent-based artificial general intelligent system that can be implemented in compact and power-efficient electronic hardware. The hardware under development is called the Street Engine, which is a hardware-based cognitive architecture for implementing agent-based artificial intelligence. In this paper, we introduce an agent-based system to replicate simple cognitive behaviours. In the processes of this system, numerical data are converted into fuzzy symbolic representations of the surrounding environment, and reasoning rules are included in a modified Fuzzy Inference System to support the cognitive decision-making. We use a case study example, the homing behaviour of the honey bee, to demonstrate constructing production rules and implementing the cognitive and reasoning capabilities of agents. The low level cognitive behaviour is converted into a rule-based fuzzy system, and hardware-based experiments have been conducted to verify the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

Evolving classifiers and especially evolving fuzzy classifiers have been established as a prominent technique for addressing the recent demands in building classifiers in an incremental open-loop manner, e.g. for the purpose of processing data streams online. So far, the focus lies on classifiers which are obtained based on the input and/or feedback in the form of target labels provided by a single user/expert. In this paper, we propose three variants of evolving multi-user fuzzy classifier systems (EFCS-MU) , where multiple users may provide their label feedback: i) ensembled single-user classifiers system , which allows a separate classifier training per user and embeds an advanced aggregation strategy (→ ensembling on a model level), ii) consensus all-user classifier system , where a joint classifier is established for all users based on consensus labelings (→ ensembling on a label level), iii) shift-work all-user classifier system , where a joint classifier is established for all users based on the classical shift-work concept. The classifiers are incrementally evolved by a single-pass learning approach embedding the autonomous evolution of new rules on demand; it integrates an unsupervised evolving clustering technique for rule partitioning, thus the same partition is established in all single-user classifiers, only the consequents in the form of class confidence vectors typically differ among the users due to their different labelings. This offers direct explainability of the varying users' annotation behaviors. The possible different experience levels of the users in relation to the process behind and possible ambiguities among the provided users' labels are handled by the proper integration of uncertainty levels into the update of the classifier(s). Furthermore, a concept is presented as to how to adequately integrate possibly available expert rules for a particular newly (on-the-fly) arising class (or in advance for several classes). Finally, an on-line active learning (oAL) strategy is demonstrated, to select only the most important samples to be labelled and thus reduce users' labeling costs, ensuring economically practicable applicability. The approach was successfully evaluated on two real-world application scenarios, one stemming for a visual inspection scenario, where four users check the quality of the imprint of compact discs and are affected by different experience levels, and one from a nursery school employment ranking application, where a new class was introduced later. The results provide insights into the performance behavior of the three different multi-user classifier variants under different circumstances (with and without expert rules, uncertainty integration, different labelling budgets etc.), including comparisons based on on-line accuracy trends versus the economy of the labeling effort. [ABSTRACT FROM AUTHOR]

This paper focuses on the design of an adaptive fuzzy filter for discrete-time multi-area power systems, considering the presence of sojourn probabilities and deception attacks. To capture the dynamic behavior of multi-area power systems more accurately, an innovative switching law is introduced, incorporating nonhomogeneous sojourn probability information. The feedback sojourn probability information is determined by a deterministic switched signal, enhancing the system's stability and performance. With the aim of addressing the potential impact of measurement outlier, a robust filter is developed, incorporating a dynamic saturation constraint and fuzzy logic systems. The filter effectively mitigates the influence of outliers by adaptively adjusting the saturation level based on estimation errors. The stability analysis is carried out using Lyapunov theory and average dwell-time information, demonstrating the mean square exponentially ultimately bounded of the augmented system. To validate the proposed algorithm, a simulation example is presented, showcasing its effectiveness and practical applicability in real-world scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a variable-type hierarchical fuzzy system (VTHFS) with high accuracy and strong interpretability. An improved rear-part direct-connected structure is used in the VTHFS, which eliminates the intermediate variables of the hierarchical fuzzy system (HFS). A new feature sorting method is proposed to find suitable input variables for each layer of subsystems of VTHFS. Under the proposed topology structure, layer by layer training of VTHFS can be achieved by changing the expected output of each layer, thereby reducing residual errors layer by layer and improving the prediction accuracy of VTHFS. Three fuzzy set distribution constraints are used in parameter optimization of VTHFS to improve the interpretability, including integrity, distinguishability, and footprint of uncertainty. The type of fuzzy set is automatically adjusted according to the uncertainty characteristics of input data measured by fuzziness, which further enhances the semantic interpretability of the VTHFS and solves the problem of requiring prior knowledge to define the fuzzy set type in existing research. Finally, the proposed VTHFS is tested on the regression data sets in the real world. Experimental results demonstrate the effectiveness of the feature sorting method and the fuzzy set type adjustment method. Through a large number of quantitative indicators, it can be confirmed that VTHFS has stronger interpretability and higher prediction accuracy compared to other fuzzy systems. [ABSTRACT FROM AUTHOR]

A rule-based system is a widely used artificial intelligence system that employs a set of rules to make decisions. The belief rule-based (BRB) classification system is an extension of fuzzy rule-based (FRB) system that handles uncertainty and imprecision in classification tasks by incorporating Dempster-Shafer evidence theory and fuzzy set theory. However, the BRB classification system suffers from the combinatorial explosion and high time complexity problems. To solve these issues, the fuzzy unordered rule induction algorithm (FURIA) is introduced into the BRB classification systems to design a novel BRB classification system in this paper. FURIA is computationally less complex and has superior classification performance. The proposed system outperforms BRB classification systems in terms of classification performance and significantly reduces the number of rules and conditions. Furthermore, the proposed system offers a more effective solution than FURIA. To evaluate the validity and superiority of the proposed system, we conduct two classification experiments, comparing it with five traditional classifiers and seven rule-based systems, respectively, in terms of classification performance and interpretability. [ABSTRACT FROM AUTHOR]

A variety of fuzzy sets, especially Pythagorean fuzzy sets (PFSs), are commonly utilized to describe the ambiguity of information in three-way decision (3WD) models. PFSs can handle fuzzy information well but ignore the description of stochastic information. To address this issue, in this paper, we attempt to construct a novel three-way multiattribute decision model by combining 3WD with constrained Pythagorean fuzzy sets (CPFSs) which can measure the reliability of PFSs. Firstly, we introduce the concept of constrained Pythagorean fuzzy information systems (CPFISs). Comprehensive positive and negative similarities of objects are given in a CPFIS by introducing normalization, score function, comparison rules, and positive and negative ideal solutions. Secondly, by comprehensive positive and negative similarities, novel formulas for calculating comprehensive relative utility function values are obtained and an objective method for estimating conditional probabilities is presented in the absence of decision attributes. Based on the above knowledge, we establish a similarity-based three-way multiattribute decision model in a CPFIS. Furthermore, we conduct a case analysis of appendicitis to realize the application of our proposed model and several comparative analyses to demonstrate the effectiveness and superiority of our model. Finally, the good performance of the new model is verified through experimental analyses. [ABSTRACT FROM AUTHOR]

In this paper, fuzzy fault-tolerant tracking control is investigated for high-order nonstrict feedback nonlinear systems in fixed time interval. The gain fault and bias fault in actuator are considered for high-order nonstrict feedback nonlinear systems. Contrary to the existing results, an adaptive estimation tactic is proposed to estimate the coexisting uncertainties such that no priori knowledge is needed for the adaptive parameters, in spite of unknown virtual control gains, nonlinear uncertainties, time-varying disturbances and actuator faults. Based on this tactic, the unknown virtual control gains are slackened to completely unknown, which is more practical. Unlike the general fixed time control tactics, the singularity problem has been overthrew by using the inequality transformation strategy without destroying the control performance. The growth assumptions are removed and the complex nonstrict feedback structure is overcome with the support of fuzzy logic systems (FLS). Finally, two examples demonstrate the validity of the formulated method. [ABSTRACT FROM AUTHOR]

This paper deals with the guaranteed cost extended dissipative stabilization problem of switched IT-2 fuzzy systems (SFSs) via intermittent control. Firstly, we propose a new class of system performance coefficients, taking both the extended dissipative performance and quadratic cost function performance into account. Then, by using a switched-path-dependent fuzzy Lyapunov functions (SFLFs) approach, the membership-function-shape-dependent (MFSD) existence conditions of a set of intermittent controllers are established. Furthermore, we apply the designed intermittent control scheme to three practical control problems: 1) A controller/actuator-failure-triggered intermittent controller is designed, and the guaranteed cost extended dissipative performance of SFSs is ensured in the presence of controller/actuator failures; 2) A switching-triggered intermittent controller is designed, and the guaranteed cost extended dissipative performance of SFSs is ensured in the presence of transmission delay of switching signal, and the asynchronous phenomena is eliminated; 3) A hybrid-triggered intermittent controller is designed, and the guaranteed cost extended dissipative performance of SFSs is ensured while the control resources are saved. Finally, the effectiveness of the results are demonstrated through a robot arm model. [ABSTRACT FROM AUTHOR]

• A novel virtual-clock-dependent IT2 fuzzy intermittent controller design scheme is proposed. It is shown that compared with the existing IT2 fuzzy controller design scheme, less conservative results can be obtained by this novel scheme. • A novel modeling method and a novel VMQLFs design scheme are presented for discrete-time switched systems with intermittent control inputs. The N -mode discrete-time switched system with intermittent control inputs can be regarded as an equivalent discrete-time switched system with 2 N modes. The informations of switching paths and virtual-clock-dependent timer are both considered in the designed VMQLFs. • By using the VMQLFs approach, the intermittent H ∞ control problem of DSIT2FSs under minimum dwell time switching is addressed for the first time. This paper is devoted to the study of intermittent H ∞ control problem for discrete-time switched interval type-2 fuzzy systems (DSIT2FSs) under minimum dwell time switching. A novel virtual-clock-dependent H ∞ intermittent interval type-2 (IT2) fuzzy controller design scheme is proposed. Firstly, we transform the N -mode DSIT2FS with intermittent control inputs into a new one with N controller-busy modes and N controller-free modes. Then, by constructing a class of non-increasing virtual-clock-dependent multiple quadratic Lyapunov functions (VMQLFs), sufficient conditions on the existence of the H ∞ intermittent IT2 fuzzy controllers are obtained for DSIT2FSs with dwell time constraints. The controller gains can be gotten by solving a set of strict linear matrix inequalities (LMIs). The obtained results are also applicable to non-switched IT2 fuzzy systems. It is shown that the designed IT2 fuzzy controllers work intermittently but still can guarantee stability and H ∞ performance of the close-loop systems. Finally, a tunnel diode circuit system model and a single link robot arm model are provided to illustrate the effectiveness and advantages of the results. [ABSTRACT FROM AUTHOR]

General type-2 fuzzy systems (GT2 FLSs) provide a more flexible way of overcoming an uncertain lack of uniformity in different applications. Centroid type reduction is one of the major component of GT2 FLSs, it is currently one of the key factors restricting GT2 FLSs efficiency. This paper proposes a novel and efficient method for centroid type reduction. The method is based on α -plane representation, where a general type-2 fuzzy set is decomposing into a series of α planes. The centroid for each plane is the calculated, layer by layer, from the top down, until the α = 0 plane is reached. In each α plane, a centroid type reduction calculation is performed using an improved enhanced opposite direction searching algorithm (IEODS). Finally, the centroids obtained for each plane are aggregated to obtain a type-1 fuzzy set, which form the centroid of general type-2 fuzzy set. Experiments show that this method results in less calculation time and fewer iterations than other α -plane representation-based reduction methods. By providing more efficient type reduction, the method improves the applicability of general type-2 fuzzy systems in more complex environments and embedded platforms, thereby enhancing their scope for future development. [ABSTRACT FROM AUTHOR]

• Unlike the existing inequality constraints between the MFs in the systems and those in the residual generators, an improved matching MF method is proposed, and the equality constraints are established. • Under the technical framework of the proposed method, the MFs of the residual generator can be obtained directly without calculation. • The proposed technical scheme for handling the asynchronous problem of PVs can overcome the limitations and defects of the existing methods. This paper investigates the design of event-triggered (ET) fault detection (FD) observers for Takagi-Sugeno (T-S) fuzzy systems. In order to provide design flexibility, the membership functions (MFs) of the residual generator are different from those of the system and the observer. Unlike the existing asynchronous premise strategies that are designed based on inequality constraints between the MFs in the systems and those in the residual generators, an improved matching MF method is proposed, and the equality constraints are established under the proposed technical framework, such that the MFs of the residual generator can be obtained directly without calculation. By applying the constraints of the fuzzy weighting parameters and the differences of each MFs, new criteria in terms of linear matrix inequalities (LMIs) are derived to ensure the desired performance of the FD system. It is shown that the proposed method can overcome the shortcomings of the existing results. Finally, the effectiveness of the proposed FD scheme is verified by an example. [ABSTRACT FROM AUTHOR]

The finite-time event-triggered H ∞ fuzzy output feedback control problem is investigated in this paper for a class of nonlinear systems. The considered nonlinear system is first modeled by an interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy system with the modeling errors. A state observer and an event-triggered mechanism are then designed by using the sampled estimating states and measured output signals. Based on the designed state observer and event-triggered mechanism, an observer-based finite-time event-triggered H ∞ fuzzy controller is developed. The sufficient conditions of guaranteeing the finite-time stability of the addressed system are established in the framework of linear matrix inequalities (LMIs). Furthermore, a computational algorithm of solving observer and controller gain matrices is given in terms of the established sufficient conditions. Finally, a practical simulation example is provided to demonstrate the effectiveness of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, fuzzy relational inequalities with addition-Łukasiewicz-product compositions are first introduced for describing the urban sewage treatment systems. It's well known that a classical fuzzy relational system usually consists of two composed operations. The most commonly used composed operations consists of max–min and max-product. But in our introduced fuzzy relational system, there exist three composed operations, i.e., addition-Łukasiewicz-product. Due to the specific composed operations, the construction for the corresponding solution set is much different. For a consistent fuzzy relation inequality system, composed by addition-Łukasiewicz-product, there exist some maximal solutions and a unique minimum solution. Especially, the number of the maximal solutions might be infinite. In fact, the solution set can be constructed by means of these maximal solutions and the sole minimum solution. Properties of the solutions to an addition-Łukasiewicz-product system is investigated. Following the presented properties, we further study the corresponding single-level and bilevel optimization problems. Two effective resolution approaches are developed and demonstrated by some numerical experiments. [ABSTRACT FROM AUTHOR]

This paper proposes the design of a robust Takagi–Sugeno (T–S) fuzzy reconfiguration block for systems with unmeasured premise variables. The reconfiguration block is composed of a virtual sensor and a virtual actuator whose design is obtained by means of new conditions based on Linear Matrix Inequalities (LMIs) with guaranteed H ∞ performance. The main contribution of the new proposed fault hiding approach concerning the current methodologies in the literature is to deal with systems subject to sensor and actuator faults with unmeasured premise variables, including the cases in which the measurements of the premise variables are affected by sensor faults. Besides, using the polytopic representation of the faults, the reconfiguration block design provides to the system robustness to a set of different sensor and actuator faults that may occur (including total and partial faults of different magnitudes). A real-time experiment consisting of two coupled tanks described as a T–S fuzzy model subject to sensor and actuator faults, with unmeasured premise variable, illustrates the performance of the proposed methodology. The experiment shows that the proposed approach can compensate the different types of faults, even when the measurement of one of the premise variables is affected by the fault. [ABSTRACT FROM AUTHOR]

This paper addresses the exponential stabilization of the switched interval type-2 fuzzy (IT-2F) time-delay systems under admissible edge-dependent average dwell time (AEDADT) scheme with a non-fragile memory state feedback controller. The IT-2F model depicts uncertain nonlinear systems where the lower and upper membership functions characterize the uncertain parameters. Unlike the previous works that relied on average dwell time (ADT), this work employs the concepts of slow switching and fast switching based on the AEDADT property for both stable and unstable modes. The main reason behind the integration is that fast switching has a great feature in restricting the unstable subsystems. Furthermore, in most nonlinear systems, the feedback control rate is realized by memoryless state feedback control. However, a time lag occurs between system state and control response. Therefore, a non-fragile memory state feedback controller is chosen to ensure the globally uniformly exponential stability condition for proposed nonlinear systems through the linear matrix inequalities (LMI) approach. A mode-dependent average dwell time (MDADT) switching scheme is compared with the described switching rule and derived the superiority of obtained results. The results demonstrate that the AEDADT switching rule is more flexible and achieves tighter bounds than the MDADT property. Finally, numerical simulations and comparisons are provided to illustrate the capability of proposed theoretical results. [ABSTRACT FROM AUTHOR]