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

Publications in this journal

  • [Show abstract] [Hide abstract]
    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: 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: 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: The adaptive fuzzy identification and control problems are considered for a class of multi-input multi-output nonlinear systems with unknown functions and unknown dead-zone inputs. The main characteristics of the considered systems are that 1) they are composed of n subsystems and each subsystem is in nested lower triangular form, 2) dead-zone inputs are in nonsymmetric nonlinear form, and 3) dead-zone inputs appear nonlinearly in the systems and their parameters are not required to be known. The controller design for this class of systems is a difficult and complicated task because of the existences of unknown functions, the couplings among the nested subsystems, and the dead-zone inputs. In the controller design, the fuzzy logic systems are employed to approximate the unknown functions and the differential mean value theorem is used to separate dead-zone inputs. To compensate for dead-zone inputs, the compensative terms are designed in the controllers. The stability of the closed-loop system is proved via the Lyapunov stability theorem. A simulation example is provided to validate the feasibility of the approach.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1387-1398. DOI:10.1109/TFUZZ.2014.2360954
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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: 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: 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: Carpooling is a means of vehicle sharing by which drivers share their cars with one or more riders whose travel itineraries are similar to their own. As such, carpooling can be an effective way to ease traffic congestion. In this paper, we first present an intelligent carpool system based on the service-oriented architecture. Second, we propose a fuzzy-controlled genetic-based carpool algorithm by using the combined approach of the genetic algorithm and the fuzzy control system, with which to optimize the route and match assignments of the providers and the requesters in the intelligent carpool system. In regard to the quality of the match solutions and processing time, the exhaustive algorithm, the random matching algorithm, and the standard genetic algorithm are applied and their results compared with those produced by our proposed algorithm. Our experimental results proved that the proposed fuzzy-controlled genetic-based carpool algorithm is capable of consistently finding carpool route and matching results that are among the most optimal solutions that can be obtained via the exhaustive algorithm and, thus, outperforming all other compared methods in regard to match quality. In addition, the proposed algorithm is also able to operate with significantly less computational time than does the exhaustive algorithm and random matching algorithm.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1698-1712. DOI:10.1109/TFUZZ.2014.2374194
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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 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: 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: 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: 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: Incorporating prior knowledge into data mining is an interesting but challenging problem, and this study proposes a novel fuzzy support vector machine (SVM) model to explore this issue. It considers the fact that in many applications, each input point may not be exactly labeled as one particular class, and thus, it applies a fuzzy membership to each input point. It also utilizes expert knowledge concerning the monotonic relations between the response and predictor variables, which is represented in the form of monotonicity constraints. We formulate the classification problem of a monotonically constrained fuzzy SVM, called a monotonic FSVM, derive its dual optimization problem, and theoretically analyze its monotonic property. The Tikhonov regularization method is further applied to ensure that the solution is unique and bounded. A new measure, i.e., the frequency monotonicity rate, is proposed to evaluate the ability of the model to retain the monotonicity. The results of the experiments on real-world and synthetic datasets show that this method, which considers different contributions of each data and the prior knowledge of the monotonicity, has a number of advantages with regard to predictive ability and retaining monotonicity over the original FSVM and SVM models when applied to classification problems.
    IEEE Transactions on Fuzzy Systems 10/2015; 23(5):1713-1727. DOI:10.1109/TFUZZ.2014.2374214