IEEE Transactions on Fuzzy Systems (IEEE T FUZZY SYST)

Publisher: IEEE Neural Networks Council; Institute of Electrical and Electronics Engineers, Institute of Electrical and Electronics Engineers

Journal description

Publishes high quality technical papers in the theory and applications of fuzzy systems with emphasis on engineering systems and scientific applications. Papers will highlight technical knowledge, exploratory developments and applications of fuzzy systems.

Current impact factor: 8.75

Impact Factor Rankings

2015 Impact Factor Available summer 2016
2014 Impact Factor 8.746
2013 Impact Factor 6.306
2012 Impact Factor 5.484
2011 Impact Factor 4.26
2010 Impact Factor 2.683
2009 Impact Factor 3.343
2008 Impact Factor 3.624
2007 Impact Factor 2.137
2006 Impact Factor 1.803
2005 Impact Factor 1.701
2004 Impact Factor 1.373
2003 Impact Factor 1.69
2002 Impact Factor 1.324
2001 Impact Factor 1.511
2000 Impact Factor 1.873
1999 Impact Factor 1.596
1998 Impact Factor 1.239
1997 Impact Factor 1.597
1996 Impact Factor 1.925

Impact factor over time

Impact factor

Additional details

5-year impact 7.88
Cited half-life 6.70
Immediacy index 0.99
Eigenfactor 0.01
Article influence 1.66
Website IEEE Transactions on Fuzzy Systems website
Other titles IEEE transactions on fuzzy systems, Institute of Electrical and Electronics Engineers transactions on fuzzy systems, Fuzzy systems, TFS
ISSN 1063-6706
OCLC 26109022
Material type Periodical, Internet resource
Document type Journal / Magazine / Newspaper, Internet Resource

Publisher details

Institute of Electrical and Electronics Engineers

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  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper focuses on the problem of approximation-based adaptive fuzzy tracking control for a class of stochastic nonlinear time-delay systems with a nonstrict-feedback structure. A variable separation approach is introduced to overcome the design difficulty from the nonstrict-feedback structure. Mamdani-type fuzzy logic systems are utilized to model the unknown nonlinear functions in the process of controller design, and an adaptive fuzzy tracking controller is systematically designed by using a backstepping technique. It is shown that the proposed controller guarantees that all signals in the closed-loop system are fourth-moment semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in the sense of mean quartic value. Simulation results are provided to demonstrate the effectiveness of our results. Further developments will consider how to generalize the proposed strategy to nonstrict-feedback nonlinear systems with input nonlinearities.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1746-1760. DOI:10.1109/TFUZZ.2014.2375917
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    ABSTRACT: This paper proposes fuzzy adaptive output feedback tracking control method for VTOL (vertical takeoff and landing) aircraft. Since uncertainties exist in the input coupling parameter due to its dependence on the physical parameters, and unknown disturbances can occur as well in the form of input-dependent accelerations in dynamics, which should be compensated at the same time. To this end, the dynamics with input-dependent disturbances are first rigorously derived based on the general form of acceleration disturbance vector in VTOL system. Second, the nonlinear velocity observer is designed to estimate the velocity information not available in the actual situation such that the output feedback tracking control can be achieved even in the presence of uncertain input coupling and disturbances, which is not possible in the case of previous works. Third, fuzzy adaptive observer is designed to estimate both unknown input coupling coefficient and input-dependent disturbances. Finally, the fuzzy adaptive output tracking controller is designed based on the nonlinear velocity observer and the fuzzy adaptive observer. The trajectory tracking errors can be guaranteed to be globally ultimately bounded and their ultimate bounds can be adjusted appropriately. Both stability analysis and simulation results demonstrate the effectiveness of the proposed method against uncertainties and disturbances in VTOL system.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1505-1518. DOI:10.1109/TFUZZ.2014.2362554
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    ABSTRACT: We present a Bayesian probabilistic model and inference algorithm for fuzzy clustering that provides expanded capabilities over the traditional Fuzzy C-Means approach. Additionally, we extend the Bayesian Fuzzy Clustering model to handle a variable number of clusters and present a particle filter inference technique to estimate the model parameters including the number of clusters. We show results on synthetic and real data and compare with other approaches.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1545-1561. DOI:10.1109/TFUZZ.2014.2370676
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    ABSTRACT: As a mixture of a random variable and an uncertain variable, an uncertain random variable is a tool to deal with the indeterminacy quantities involving randomness and human uncertainty. This paper aims at proposing a concept of an uncertain random alternating renewal process to model a repairable system with random on-times and uncertain off-times. An alternating renewal theorem is proved, which gives the limit chance distribution of the interval availability of the uncertain random alternating renewal system.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1333-1342. DOI:10.1109/TFUZZ.2014.2360551
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    ABSTRACT: One of the most important elements of social network analysis is community detection, i.e., finding groups of similar people based on their traits. In this paper, we present the fuzzy modularity maximization (FMM) approach for community detection, which finds overlapping—that is, fuzzy—communities (where appropriate) by maximizing a generalized form of Newman's modularity. The first proposed FMM solution uses a tree-based structure to find a globally optimal solution, while the second proposed solution uses alternating optimization to efficiently search for a locally optimal solution. Both of these approaches are based on a proposed algorithm called one-step modularity maximization (OSMM), which computes the optimal cluster memberships for one person in the social network. We prove that OSMM can be formulated as a simplified quadratic knapsack optimization problem, which is $O(n)$ time complexity. We then propose a tree-based algorithm, called FMM/Find Best Leaf Node (FMM/FBLN), which represents sequences of OSMM steps in a tree-based structure. It is proved that FMM/FBLN finds globally optimal solutions for FMM; however, the time complexity of FMM/FBLN is $O(n^d)$, $dge 2$; thus, it is impractical for most real-world networks. To combat this inefficiency, we propose five heuristic-based alternating optimization schemes, i.e., FMM/H1–H5, which are all shown to be $O(n^2)$ time complexity. We compare the results of the FMM/H solutions with those of state-of-the-art community detection algorithms, MULTICUT spectral FCM (MSFCM) and GALS, and with those of two fuzzy community detection algorithms called GA and vertex-similarity based gradient-descent method (VSGD) on ten real-world datasets. We conclude that one of the fi- e heuristic algorithms (FMM/H2) is very competitive with GALS and much more effective than MSFCM, GA, and VSGD. Furthermore, all of the FMM/H schemes are at least two orders of magnitude faster than GALS in run time. Finally, FMM/H, unlike GALS (which only produces crisp partitions) and MSFCM (which always finds fuzzy partitions), is the only fuzzy community detection algorithm to date that can find the max-modularity partition, fuzzy or crisp.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1356-1371. DOI:10.1109/TFUZZ.2014.2360723
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    ABSTRACT: Many different real-world applications with a high-level of uncertainty proved the good performance of the type-2 fuzzy sets (T2 FSs). Balanced diet means that the intake of each necessary nutrient meets its adequate demand and actual caloric intake balances with calories burned. Additionally, making a diversity of choice from various types of food is also essential to reduce the risk of developing various chronic diseases. Different people have a different goal and it is hard to measure how healthy the eaten meal is for those who are not the domain experts on the diet. This paper presents an adaptive personalized diet linguistic recommendation mechanism based on type-2 fuzzy logic system (T2 FLS) and genetic fuzzy markup language (GFML). First, an adaptive dietary assessment and recommendation ontology is constructed by domain experts, and then a T2 FS-based GFML, describing the fuzzy knowledge base and the fuzzy rule base of the proposed mechanism, is evolved by using genetic algorithms. Next, a T2 FS-based fuzzy inference mechanism infers the result of the dietary health level based on the evolved type-2 GFML (T2GFML). In addition, the balanced computation mechanism is also proposed to reduce the computational complexity of the T2 FLS for the diet domain knowledge. Finally, the linguistic knowledge discovery mechanism presents the discovered linguistic meaning about the meal's health level to show the involved subjects how to make a personalized diet linguistic recommendation. This type of information about the eaten meal can provide the subjects with a reference to gradually improve their unhealthy eating habit and then become healthier and healthier. Experimental results show that the results of the proposed mechanism for the T2 FLS are better than those for the type-1 fuzzy logic system (T1 FLS).
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1777-1802. DOI:10.1109/TFUZZ.2014.2379256
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    ABSTRACT: This paper is concerned with the problem of dissipative control for Takagi–Sugeno fuzzy systems under time-varying sampling with a known upper bound on the sampling intervals. Based on the time-dependent Lyapunov–Krasovskii functional approach, which makes full use of the available information about the actual sampling pattern, a sufficient condition is established to guarantee the sampled-data systems to be exponentially stable and strictly $(mathcal {Q},mathcal {S},mathcal {R})$- $gamma$-dissipative. Based on the criterion, a design algorithm for the desired sampled-data controller is proposed. The effectiveness and benefits of the results developed in this paper is demonstrated by a controller design for a truck-trailer system.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1669-1679. DOI:10.1109/TFUZZ.2014.2374192
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    ABSTRACT: Fuzzy regression methods have commonly been used to develop consumer preferences models, which correlate the engineering characteristics with consumer preferences regarding a new product; the consumer preference models provide a platform, whereby product developers can decide the engineering characteristics in order to satisfy consumer preferences prior to developing the products. Recent research shows that these fuzzy regression methods are commonly used to model customer preferences. However, these approaches have a common limitation in that they do not investigate the appropriate polynomial structure, which includes significant regressors with only significant engineering characteristics; also, they cannot generate interaction or high-order regressors in the models. The inclusion of insignificant regressors is not an effective approach when developing the models. Exclusion of significant regressors may affect the generalization capability of the consumer preference models. In this paper, a novel fuzzy modeling method is proposed, namely fuzzy stepwise regression (F-SR), in order to develop a customer preference model which is structured with an appropriate polynomial, which includes only significant regressors. Based on the appropriate polynomial structure, the fuzzy coefficients are determined using the fuzzy least-squares regression. The developed fuzzy regression model attempts to obtain a better generalization capability using a smaller number of regressors. The effectiveness of the F-SR is evaluated based on two design problems, namely a tea maker design and a solder paste dispenser design. Results show that better generalization capabilities can be obtained compared with the fuzzy regression methods commonly used for new product development. In addition, smaller scale consumer preference models with fewer engineering characteristics can be obtained. Hence, a simpler and more effective product development platform can be provided.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1728-1745. DOI:10.1109/TFUZZ.2014.2375911
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    ABSTRACT: Solving optimization problems under hybrid uncertainty bears a heavy computational burden. In this study, we propose a unified structured optimization approach, termed robust granular optimization (RGO), to tackle the optimization problems under hybrid manifold uncertainties in a computationally tractable manner. Essentially, the RGO can be regarded as a complementary fusion of granular computing and robust optimization techniques. The paradigm of RGO consists of three core phases: 1) uncertainty identification, 2) information granulation in which basic granular units (BGUs) are formed, and 3) robust optimization realized over the BGUs. Following the proposed paradigm, we develop two classes of RGO models for general single-stage and two-stage optimization problems with separable and higher order hybrid uncertainties, respectively. It is shown that both types RGO models can be equivalently transformed into linear programs or mixed integer linear programs that can be handled efficiently by off-the-shelf solvers. Furthermore, a target-based tradeoff model is developed to enhance the flexibility of the RGO models in balancing the granularity level (or robustness level) and the solution conservativeness. The tradeoff model can also be efficiently solved by a binary search algorithm. Finally, sufficient computational studies are presented, and comparisons with the existing approaches show that the RGO models can bring much higher computational efficiency and scalability without losing much optimality, and the RGO solutions exhibit a stronger resistance to the uncertainty.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1372-1386. DOI:10.1109/TFUZZ.2014.2360941
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    ABSTRACT: This paper deals with tanker steering control based on a novel multiple-input multiple-output generalized ellipsoidal-basis-function-based fuzzy neural network (GEBF-FNN) with online updating of system structure and parameters. The main contributions of this paper are as follows. 1) A GEBF-FNN-based nonlinear steering model incorporating the nonlinearity underlying tanker dynamics is proposed. 2) The static local controller (SLC), whose controller gains are locally fixed with the initial forward speed and the desired heading for individual steering commands, is implemented. 3) The dynamic local controller (DLC) is further realized by employing adaptive controller gains pertaining to time-varying forward speed and heading dynamics. 4) The GEBF-FNN-based steering controller is developed by identifying a nonlinear mapping from the heading error, acceleration and forward speed to dynamic controller gains, and thereby contributing to a model-free adaptive control scheme. Simulation results and comprehensive studies on benchmark problems demonstrate that the GEBF-FNN-based model can capture the essential tanker dynamics, and the proposed SLC, DLC, and GEBF-FNN-based schemes achieve superior performance in terms of heading regulation and forward speed loss. In comparison with the SLC and traditional fuzzy controllers, the DLC and GEBF-FNN-based controllers achieve higher accuracy of heading regulation with less rudder efforts and minimal forward speed losses.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1414-1427. DOI:10.1109/TFUZZ.2014.2362144
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    ABSTRACT: This paper proposes an interval type-2 neural fuzzy classifier learned through soft margin minimization (IT2NFC-SMM) and applies it to human body posture classification. The IT2NFC-SMM consists of interval type-2, zero-order Takagi–Sugeno (T–S) fuzzy rules established through online structure learning. The antecedent part of the IT2NFC-SMM uses interval type-2 fuzzy sets to decrease the number of rules and manage noisy data. For parameter learning, the consequent parameters are learned through a linear support vector machine (SVM) for soft margin minimization to improve the generalization ability. The proposed SVM-based learning addresses the problem that the orders of the fuzzy rules in computing the outputs of an interval type-2 fuzzy system depend on the consequent values that are unknown in advance. To address this problem, the IT2NFC-SMM uses weighted bound-set boundaries to simplify the type-reduction operation and a novel crisp-to-interval linear SVM learning algorithm. Based on the soft margin minimization, the antecedent parameters are tuned using the gradient descent algorithm. The IT2NFC-SMM is applied to a vision-based human body posture classification system. The system uses two cameras and novel classification features extracted from a silhouette of the human body to classify the four postures of standing, bending, sitting, and lying. The classification performance of the IT2NFC-SMM is verified through results in clean and noisy classification examples and through the posture classification problem, as well as through comparisons with various type-1 and type-2 fuzzy classifiers. The overall result shows that the IT2NFC-SMM achieves higher classification rates with a smaller or similar model size than the classifiers used for comparison, especially for noisy classification problems.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1474-1487. DOI:10.1109/TFUZZ.2014.2362547
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    ABSTRACT: Spatiotemporal data, in particular fuzzy and complex spatial objects representing geographic entities and relations, is a topic of great importance in geographic information systems and environmental data management systems. For database researchers, modeling and designing a database of fuzzy spatiotemporal data and querying such a database efficiently have been challenging issues due to complex spatial features and uncertainty involved. This paper presents an integrated approach to modeling, indexing, and efficiently querying spatiotemporal data related to fuzzy spatial and complex objects and spatial relations. As our case study, we design and implement a meteorological database application that involves fuzzy spatial and complex objects, and a spatiotemporal index structure, and supports various types of spatial queries including fuzzy spatiotemporal queries. Our implementation is based on an intelligent database system architecture that combines a fuzzy object-oriented database with a fuzzy knowledge base.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1399-1413. DOI:10.1109/TFUZZ.2014.2362121
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    ABSTRACT: We suggest a transformation to obtain the negation of a probability distribution. We investigate the properties of this negation. Using the Dempster–Shafer theory of evidence, we show of all the possible negations our proposed negation is one having a maximal type entropy.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1899-1902. DOI:10.1109/TFUZZ.2014.2374211
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    ABSTRACT: This paper is concerned with the nonfragile distributed $H_infty$ filtering problem for a class of discrete-time Takagi–Sugeno (T–S) systems in sensor networks. Additive filter gain uncertainties that reflect imprecision in filter implementation are considered. Based on the robust control approach, sufficient conditions are obtained to ensure that the filtering error system is asymptotically stable with a prescribed $H_infty$ performance level and the eigenvalues of the filtering error system in a given circular region. The filter parameters are determined by solving a set of linear matrix inequalities. A simulation study on the nonlinear tunnel diode circuit system is presented to show the effectiveness of the proposed design method.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1883-1890. DOI:10.1109/TFUZZ.2014.2367101
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    ABSTRACT: In this study, a generalized fuzzy two-stage stochastic programming (GFTSP) method is developed for planning water resources management systems under uncertainty. The developed GFTSP method can deal with uncertainties expressed as probability distributions, fuzzy sets, as well as fuzzy random variables. With the aid of a robust stepwise interactive algorithm, solutions for GFTSP can be generated by solving a set of deterministic submodels. Furthermore, the possibility information (expressed as fuzzy membership functions) can be reflected in the solutions for the objective function value and decision variables. The developed GFTSP approach is also applied to a water resources management and planning problem to demonstrate its applicability. Solutions of decision variables and objective function value are expressed as fuzzy membership functions, reflecting the fluctuating ranges of decision alternatives under different plausibilities. And thus, the water alternatives can be directly derived from the obtained fuzzy membership functions when the preferred α value is predefined by decision makers.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1488-1504. DOI:10.1109/TFUZZ.2014.2362550
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    ABSTRACT: In this paper, we deal with the Choquet integral and derivative with respect to fuzzy measures on the nonnegative real line and present a way to Choquet calculus as a new research paradigm. In Choquet calculus, a representation for calculating the continuous Choquet integral is first given by restricting the integrand to a class of nondecreasing and continuous functions and the fuzzy measure to a class of distorted Lebesgue measures. Next, the derivative of functions with respect to distorted Lebesgue measures is defined as the inverse operation of the Choquet integral. Then, elementary properties in Choquet calculus are explored. In addition, we clarify the relation of Choquet calculus with fractional calculus, where the fractional Choquet integral and derivative are newly defined. In addition, we consider differential equations with respect to distorted Lebesgue measures and give their solutions. Finally, we introduce conditional distorted Lebesgue measures and explore their properties.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1439-1457. DOI:10.1109/TFUZZ.2014.2362148
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    ABSTRACT: In computing with words, establishing a fuzzy set (FS) model for a word to capture its uncertainties is an important issue. An interval type-2 (IT2) FS can be used to model a word. How to establish an IT2 FS from the collected data about a word has been a challenging problem. It has been reported that one way is to extract the centroid of an IT2 FS from the collected data and to obtain geometric parameters of its footprint of uncertainty (FOU) such that its centroid matches the extracted one. How to extract the centroid of an IT2 FS from the collected data about a word has thoroughly been studied. However, there exists no method for obtaining FOU parameters for an IT2 FS such that its centroid matches the desired one. To fill this gap, this paper presents an approach to obtaining FOU parameters for an IT2 FS by establishing equations using the centroid requirement. To propose this approach, a sufficient and necessary condition for ensuring the centroid of an IT2 FS is developed. Using this sufficient and necessary condition, two equations about all of the FOU parameters are established. To obtain the FOU parameters, all of them except two are predetermined so that the established equations can be simplified to two single-variable equations. The other two FOU parameters can then be determined by solving these two single-variable equations using existing root-finding algorithms. Among existing root-finding algorithms, the false position algorithm is recommended. The overall merits of the proposed approach are its simplicity in implementation and its applicability to IT2 FSs with arbitrary FOU shapes. In addition, numerical examples are provided to further illustrate how to apply the proposed approach to obtain FOU parameters for an IT2 FS.
    IEEE Transactions on Fuzzy Systems 08/2015; 23(4):950-963. DOI:10.1109/TFUZZ.2014.2336255
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    ABSTRACT: A critical issue when selecting an ordered weighted aggregation (OWA) operator is the determination of the associated weights. For this reason, numerous weight generating methods have appeared in the literature. In this paper, a generalization of the binomial OWA operator on the basis of the Stancu polynomial is proposed and analyzed. We propose a weight function in the parametric form using the Stancu polynomial by which the weights of OWA operators can be generated easily. The proposed Stancu OWA operator provides infinitely many sets of weight vectors for a given level of the orness value. An important property of this kind of OWA operator is its orness, which remains constant, irrespective of the number of objectives aggregated and always equal to one of its parameters. This approach provides a significant advantage for generating the OWA operators’ weights over existing methods. One can choose a set of weight vectors based on his/her own preference. This class of OWA operators can utilize a prejudiced preference to determine the corresponding weight vector. The maximum entropy (Shannon) OWA operator's weights for a given level of orness is calculated by the purposed weight function and compared with the existing maximum entropy OWA operator.
    IEEE Transactions on Fuzzy Systems 08/2015; 23(4):1306-1313. DOI:10.1109/TFUZZ.2014.2336696
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    ABSTRACT: The study is devoted to the clustering of granular data and an evaluation of the results of such clustering. A comprehensive and systematic approach is developed, which is composed of three fundamental phases: 1) representation of granular data; 2) clustering carried out in the representation space of information granules; and 3) evaluation of quality of clusters following the reconstruction criterion. The reconstruction criterion formed originally for numeric data and leading to an idea of granular prototypes is revisited. We show here an emergence of granular information of higher type, which are used to implement granular interval prototypes. We discuss a way of forming granular data in the context of representation of time series and present clustering of granular time series.
    IEEE Transactions on Fuzzy Systems 08/2015; 23(4):850-860. DOI:10.1109/TFUZZ.2014.2329707