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: 6.31

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 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 4.89
Cited half-life 7.40
Immediacy index 0.58
Eigenfactor 0.01
Article influence 1.20
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: 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: 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: 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
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    ABSTRACT: This paper presents a universal methodology for generating an interval type-2 fuzzy set membership function from a collection of type-1 fuzzy sets. The key idea of the proposed methodology is to designate a specific type-1 fuzzy set as the representative of all input type-1 fuzzy sets. To this end, we use a novel measure of similarity between type-1 fuzzy sets, which relies on both kernel functions and fuzzy information processing methods. Based on the selected representative type-1 fuzzy set, and with respect to the principle of justifiable granularity, an interval type-2 fuzzy set is then formed. The results of the conducted experiments demonstrate the effectiveness of the proposed methodology for generating sound interval type-2 fuzzy sets.
    IEEE Transactions on Fuzzy Systems 08/2015; 23(4):1014-1029. DOI:10.1109/TFUZZ.2014.2336673
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    ABSTRACT: The interval type-2 fuzzy Proportional-Integral (PI) controller (IT2-FPI) might be able to handle high levels of uncertainties to produce a satisfactory control performance, which could be potentially due to the robust performance as a result of the smoother control surface around the steady state. However, the transient state and disturbance rejection performance of the IT2-FPI may degrade in comparison with the type-1 fuzzy PI (T1-FPI) counterpart. This drawback can be resolved via general type-2 fuzzy PI controllers which can provide a tradeoff between the robust control performance of the IT2-FPI and the acceptable transient and disturbance rejection performance of the type-1 PI controllers. In this paper, we will present a zSlices-based general type-2 fuzzy PI controller (zT2-FPI), where the secondary membership functions (SMFs) of the antecedent general type-2 fuzzy sets are adjusted in an online manner. We will examine the effect of the SMF on the closed-system control performance to investigate their induced performance improvements. This paper will focus on the case followed in conventional or self-tuning fuzzy controller design strategies, where the aim is to decrease the integral action sufficiently around the steady state to have robust system performance against noises and parameter variations. The zSlices approach will give the opportunity to construct the zT2-FPI controller as a collection of IT2-FPI and T1-FPI controllers. We will present a new way to design a zT2-FPI controller based on a single tuning parameter where the features of T1-FPI (speed) and IT2-FPI (robustness) are combined without increasing the computational complexity much when compared with the IT2-FPI structure. This will allow the proposed zT2-FPI controller to achieve the desired transient state response and provide an efficient disturbance rejection and robust control performance. We will present several simulation studies on benchmark systems, in addition to real-world experime- ts that were performed using the PIONEER 3-DX mobile robot that will act as a platform to evaluate the proposed systems. The results will show that the control performance of the self-tuning zT2-FPI control structure enhances both the transient state and disturbance rejection performances when compared with the type-1 and IT2-FPI counterparts. In addition, the self-tuning zT2-FPI is more robust to disturbances, noise, and uncertainties when compared with the type-1 and interval type-2 fuzzy counterparts.
    IEEE Transactions on Fuzzy Systems 08/2015; 23(4):991-1013. DOI:10.1109/TFUZZ.2014.2336267
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    ABSTRACT: In this paper, the problem of induced $L_{2}$ disturbance attenuation control is investigated for discrete-time Takagi–Sugeno (T–S) fuzzy delta operator systems with time-varying delays and disturbance input via an input–output approach. A model transformation method is utilized to approximate the time-varying delay in T–S fuzzy delta operator systems. By applying the scaled small gain (SSG) theorem and Lyapunov–Krasovskii functional approach, a sufficient condition is established to guarantee that the closed-loop system is asymptotically stable and has an induced $L_{2}$ disturbance attenuation performance. The existence condition of the dynamic output-feedback controller can be solved via convex optimization problems. Finally, simulation results are given to demonstrate the feasibility and effectiveness of the proposed method.
    IEEE Transactions on Fuzzy Systems 08/2015; 23(4):1100-1112. DOI:10.1109/TFUZZ.2014.2346237
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes sum-of-squares (SOS) methodologies for stability analysis and region-of-attraction (ROA) estimation for nonlinear systems represented by polynomial fuzzy models via piecewise polynomial Lyapunov functions (PPLFs). At first, two SOS-based global stability criteria are proposed by applying maximum-type and minimum-type PPLFs, respectively. It is known that less-conservative results can be obtained by reducing global stability to local stability, since it is usually the case for nonlinear systems that the stability cannot be reached globally. Therefore, based on the two types of PPLFs, two local stability criteria are further proposed with the algorithms that enlarge the estimated ROA as much as possible. The constraints for checking (global and local) stability and enlarging the estimated ROA are represented in terms of bilinear SOS problems. Hence, the path-following method is applied to solve the bilinear SOS problems in the proposed methodologies. Finally, some examples are provided to illustrate the utility of the proposed methodologies.
    IEEE Transactions on Fuzzy Systems 08/2015; 23(4):1314-1322. DOI:10.1109/TFUZZ.2014.2347993
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    ABSTRACT: The aim of this paper is to develop a new methodology for solving matrix games with payoffs of Atanassov's intuitionistic fuzzy (I-fuzzy) numbers. In this methodology, we define the concepts of I-fuzzy numbers and the value-index and ambiguity-index and develop a difference-index-based ranking method, which is proven to be a total order. By doing this, the parameterized nonlinear programming models are derived from a pair of auxiliary I-fuzzy mathematical programming models, which are used to determine solutions of matrix games with payoffs of I-fuzzy numbers. The validity and applicability of the models and method proposed in this paper are illustrated with a practical example.
    IEEE Transactions on Fuzzy Systems 08/2015; 23(4):885-896. DOI:10.1109/TFUZZ.2014.2333065
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    ABSTRACT: Our concern here is with the issue of the determination of the satisfaction (firing level) of the antecedent condition associated with a variable in a fuzzy systems rule base where this antecedent condition is expressed in terms of a normal fuzzy set, linguistic value. We first consider the case where the input information about the variable is also expressed in terms of a normal fuzzy set. After looking at some approaches for determining this firing level we provide the requirements needed by any formulation for this operation when our input information is a fuzzy set. We next introduce the idea of a measure and show how it can be used to more generally express our knowledge about an uncertain value associated with a variable. We then generalize the requirements for any formulation that can be used to determine the satisfaction (firing level) of the antecedent fuzzy set when the input information about the variable is expressed using a measure. We further provide some examples of formulations. Since a probability distribution is a special case of a measure we are able to determine the firing level of fuzzy rules with probabilistic inputs.
    IEEE Transactions on Fuzzy Systems 08/2015; 23(4):939-949. DOI:10.1109/TFUZZ.2014.2336253
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    ABSTRACT: This paper investigates the problem of filter design for interval type-2 (IT2) fuzzy systems with D stability constraints based on a new performance index. Attention is focused on solving the $H_{infty}$, $L_{2}$– $L_{infty}$, passive, and dissipativity fuzzy filter design problems for IT2 fuzzy systems with D stability constraints in a unified frame. Under the new performance index frame, using Lyapunov stability theory, a novel type of IT2 filter is designed such that the filtering error system guarantees the prescribed $H_{infty}$, $L_{2}$–$L_{infty}$ , passive, and dissipativity performance levels with D stability constraints. The existence condition of the IT2 filter is expressed as the convex optimization problem, and the filter parameters in the condition can be solved by the standard software. The IT2 fuzzy model and IT2 fuzzy filter do not need to share the same lower and upper membership functions. Finally, a numerical example is provided to show the effectiveness of the proposed results.
    IEEE Transactions on Fuzzy Systems 06/2015; 23(3):719-725. DOI:10.1109/TFUZZ.2014.2315658
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    ABSTRACT: An interesting problem within the theory of indistinguishability operators is how to approximate an arbitrary fuzzy subset by a similar extensional one. In this paper, we aim to solve this question and provide three methods to find extensional approximations of fuzzy subsets $mu$. These methods are exhaustively explained for different Archimedean t-norms, and an example is provided to illustrate them.
    IEEE Transactions on Fuzzy Systems 06/2015; 23(3):617-626. DOI:10.1109/TFUZZ.2014.2321593