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Fuzzy Systems, IEEE Transactions on

Issue 6 • Date Dec. 2006

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  • Table of contents

    Page(s): C1
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  • IEEE Transactions on Fuzzy Systems publication information

    Page(s): C2
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  • Error Estimation of Perturbations Under CRI

    Page(s): 709 - 715
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (299 KB) |  | HTML iconHTML  

    The analysis of stability and robustness of fuzzy reasoning is an important issue in areas like intelligent systems and fuzzy control. An interesting aspect is to what extent the perturbation of input in a fuzzy reasoning scheme causes the oscillation of the output. In particular, when the error limits (restrictions) of the input values are given, what the error limits of the output values are. In this correspondence, we estimate the upper and lower bounds of the output error affected by the perturbation parameters of the input, and obtain the limits of the output values when the input values range over some interval in many fuzzy reasoning schemes under compositional rule of fuzzy inference (CRI) View full abstract»

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  • Mixed Feedforward/Feedback Based Adaptive Fuzzy Control for a Class of MIMO Nonlinear Systems

    Page(s): 716 - 727
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (486 KB) |  | HTML iconHTML  

    This paper proposes a mixed feedforward/feedback (FFB) based adaptive fuzzy controller design for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. By integrating both feedforward and feedback compensation, we introduce the FFB-based fuzzy controller composed of a feedforward fuzzy compensator and a robust error-feedback compensator. To achieve a forward compensation of uncertainties, the feedforward fuzzy compensator takes the desired commands as premise variables of fuzzy rules and adaptively adjusts the consequent part from an error measure. Meanwhile, the feedback controller part is constructed based on Hinfin control techniques and nonlinear damping design. Then, the attenuation of both disturbances and estimated fuzzy parametric errors is guaranteed from a linear matrix inequality (LMI)-based gain design. The main advantages are: i) a simpler architecture for implementation is provided; and ii) the typical boundedness of assumption on fuzzy universal approximation errors is not required. Finally, an inverted pendulum system and a two-link robot are taken as application examples to show the expected performance View full abstract»

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  • Speeding up VLSI Layout Verification Using Fuzzy Attributed Graphs Approach

    Page(s): 728 - 737
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (991 KB) |  | HTML iconHTML  

    Technical and economic factors have caused the field of physical design automation to receive increasing attention and commercialization. The steady down-scaling of complementary metal oxide semiconductor (CMOS) device dimensions has been the main stimulus to the growth of microelectronics and computer-aided very large scale integration (VLSI) design. The more an Integrated Circuit (IC) is scaled, the higher its packing density becomes. For example, in 2006 Intel's 65-nm process technology for high performance microprocessor has a reduced gate length of 35 nanometers. In their 70-Mbit SRAM chip, there are up to 0.5 billion transistors in a 110 mm2 chip size with 3.4 GHz clock speed. New technology generations come out every two years and provide an approximate 0.7 times transistor size reduction as predicted by Moore's Law. For the ultimate scaled MOSFET beyond 2015 or so, the transistor gate length is projected to be 10 nm and below. The continually increasing size of chips, measured in either area or number of transistors, and the wasted investment involving fabricating and testing faulty circuits, make layout analysis an important part of physical design automation. Layout-versus-schematic (LVS) is one of three kinds of layout analysis tools. Subcircuit extraction is the key problem to be solved in LVS. In LVS, two factors are important. One is run time, the other is identification correctness. This has created a need for computational intelligence. Fuzzy attributed graph is not only widely used in the fields of image understanding and pattern recognition, it is also useful to the fuzzy graph matching problem. Since the subcircuit extraction problem is a special case of a general-interest problem known as subgraph isomorphism, fuzzy attributed graphs are first effectively applied to the subgraph isomorphism problem. Then we provide an efficient fuzzy attributed graph algorithm based on the solution to subgraph isomorphism for the subcircuit extractio- - n problem. Similarity measurement makes a significant contribution to evaluate the equivalence of two circuit graphs. To evaluate its performance, we compare fuzzy attributed graph approach with the commercial software called SubGemini, and two of the fastest approaches called DECIDE and SubHDP. We are able to achieve up to 12 times faster performance than alternatives, without loss of accuracy View full abstract»

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  • Robust H_{\infty } Fuzzy Control Approach for a Class of Markovian Jump Nonlinear Systems

    Page(s): 738 - 754
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (621 KB) |  | HTML iconHTML  

    This paper addresses the problem of stabilizing a class of nonlinear systems subject to Markovian jump parameters using a robust stochastic fuzzy controller with Hinfin performance. The class of jump nonlinear systems considered is described by a fuzzy model composed of two levels: A crisp level which represents the jumps and a fuzzy level which represents the system nonlinearities. Considering the approximation error between the fuzzy model and the jump nonlinear system as norm-bounded uncertainties, we develop a systematic technique based on a coupled Lyapunov function to obtain a robust stochastic fuzzy controller which guarantees the L2 gain of the closed-loop system in respect to external inputs to be equal to or less than a prescribed value. A simulation example on an industrial power plant operating in a cogeneration scheme is presented to illustrate the effectiveness of the proposed stabilizing controller in reducing oscillations as well as maintaining a desired operation condition in the presence of fluctuations in the local load View full abstract»

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  • Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms

    Page(s): 755 - 766
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (560 KB) |  | HTML iconHTML  

    A novel hybrid learning algorithm based on a genetic algorithm to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on genetic algorithms (SOFNNGA), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. A new adding method based on geometric growing criterion and the epsiv-completeness of fuzzy rules is first used to generate the initial structure. Then a hybrid algorithm based on genetic algorithms, backpropagation, and recursive least squares estimation is used to adjust all parameters including the number of fuzzy rules. This has two steps: First, the linear parameter matrix is adjusted, and second, the centers and widths of all membership functions are modified. The GA is introduced to identify the least important neurons, i.e., the least important fuzzy rules. Simulations are presented to illustrate the performance of the proposed algorithm View full abstract»

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  • A Multicriteria Approach to Data Summarization Using Concept Ontologies

    Page(s): 767 - 780
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (474 KB) |  | HTML iconHTML  

    This paper describes a conceptual and theoretical framework to allow better user control over data summarization for knowledge discovery. Basic to the approach is a measure of quality of summarization of data using categories provided by the hierarchical structure of concept ontology. This involves the modeling, using a fuzzy sets approach, of the four criteria implicit in a summarization imperative: minimum coverage, minimum relevance, succinctness, and usefulness. With these criteria modeled, a multicriteria approach is presented, using a decision function aggregating these criteria that provides an overall quality measure to guide the summarization of the data. The development of the theory is first presented for the simple case of a single attribute to clearly delineate the basic issues and approach and then extended to multiple attributes. Finally, approaches to provide a more user-oriented presentation of the summarized data are considered View full abstract»

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  • Type-2 Fuzzistics for Symmetric Interval Type-2 Fuzzy Sets: Part 1, Forward Problems

    Page(s): 781 - 792
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1009 KB) |  | HTML iconHTML  

    Interval type-2 fuzzy sets (T2 FS) play a central role in fuzzy sets as models for words and in engineering applications of T2 FSs. These fuzzy sets are characterized by their footprints of uncertainty (FOU), which in turn are characterized by their boundaries-upper and lower membership functions (MF). In this two-part paper, we focus on symmetric interval T2 FSs for which the centroid (which is an interval type-1 FS) provides a measure of its uncertainty. Intuitively, we anticipate that geometric properties about the FOU, such as its area and the center of gravities (centroids) of its upper and lower MFs, will be associated with the amount of uncertainty in such a T2 FS. The main purpose of this paper (Part 1) is to demonstrate that our intuition is correct and to quantify the centroid of a symmetric interval T2 FS, and consequently its uncertainty, with respect to such geometric properties. It is then possible, for the first time, to formulate and solve forward problems, i.e., to go from parametric interval T2 FS models to data with associated uncertainty bounds. We provide some solutions to such problems. These solutions are used in Part 2 to solve some inverse problems, i.e., to go from uncertain data to parametric interval T2 FS models (T2 fuzzistics) View full abstract»

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  • Implicative Fuzzy Associative Memories

    Page(s): 793 - 807
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1695 KB) |  | HTML iconHTML  

    Associative neural memories are models of biological phenomena that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomplete version of an input pattern. In this paper, we introduce implicative fuzzy associative memories (IFAMs), a class of associative neural memories based on fuzzy set theory. An IFAM consists of a network of completely interconnected Pedrycz logic neurons with threshold whose connection weights are determined by the minimum of implications of presynaptic and postsynaptic activations. We present a series of results for autoassociative models including one pass convergence, unlimited storage capacity and tolerance with respect to eroded patterns. Finally, we present some results on fixed points and discuss the relationship between implicative fuzzy associative memories and morphological associative memories View full abstract»

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  • Interval Type-2 Fuzzy Logic Systems Made Simple

    Page(s): 808 - 821
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (899 KB) |  | HTML iconHTML  

    To date, because of the computational complexity of using a general type-2 fuzzy set (T2 FS) in a T2 fuzzy logic system (FLS), most people only use an interval T2 FS, the result being an interval T2 FLS (IT2 FLS). Unfortunately, there is a heavy educational burden even to using an IT2 FLS. This burden has to do with first having to learn general T2 FS mathematics, and then specializing it to an IT2 FSs. In retrospect, we believe that requiring a person to use T2 FS mathematics represents a barrier to the use of an IT2 FLS. In this paper, we demonstrate that it is unnecessary to take the route from general T2 FS to IT2 FS, and that all of the results that are needed to implement an IT2 FLS can be obtained using T1 FS mathematics. As such, this paper is a novel tutorial that makes an IT2 FLS much more accessible to all readers of this journal. We can now develop an IT2 FLS in a much more straightforward way View full abstract»

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  • Controller Design Under Fuzzy Pole-Placement Specifications: An Interval Arithmetic Approach

    Page(s): 822 - 836
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1113 KB) |  | HTML iconHTML  

    This paper discusses fuzzy specifications for robust controller design, as a way to define different specification levels for different plants in a family and allow the control of performance degradation. Controller synthesis will be understood as mapping a fuzzy plant onto a desired fuzzy set of closed-loop specifications. In this context, a fuzzy plant is considered as a possibility distribution on a given plant space. In particular, pole placement in linear plants with fuzzy parametric uncertainty is discussed, although the basic idea is general and could be applied to other settings. In the case under consideration, the controller coefficients are the solution of a fuzzy linear system of equations with a particular semantics. Modal interval arithmetic is used to solve the system for each alpha-cut. The intersection of the solutions, if not empty, constitutes the solution to the robust control problem View full abstract»

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  • An Orness Measure for Quasi-Arithmetic Means

    Page(s): 837 - 848
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (382 KB) |  | HTML iconHTML  

    In this paper, an orness measure to reflect the or-like degree of the quasi-arithmetic mean operator is proposed. With the generating function representation method, some properties of a quasi-arithmetic mean, associated with its orness measure, are analyzed. Then, the paper focuses on two kinds of parameterized quasi-arithmetic means with exponential function and power function generators respectively. Some properties of them are examined and compared. As the aggregation value for any set always monotonically increases with the orness level, the orness level can be used as a control parameter to represent the decision maker's preference. A generic method to get the quasi-arithmetic mean operator which makes the aggregation result consistent with its orness level is also examined. Finally, the properties of the quasi-arithmetic mean and that of the exponential and power function generators are extended to the weighted quasi-arithmetic mean by considering the importance of the aggregated elements View full abstract»

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  • Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks

    Page(s): 849 - 859
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (760 KB) |  | HTML iconHTML  

    This paper presents an adaptive nonsingular terminal sliding mode (NTSM) tracking control design for robotic systems using fuzzy wavelet networks. Compared with linear hyperplane-based sliding control, terminal sliding mode controller can provide faster convergence and higher precision control. Therefore, a terminal sliding controller combined with the fuzzy wavelet network, which can accurately approximate unknown dynamics of robotic systems by using an adaptive learning algorithm, is an attractive control approach for robots. In addition, the proposed learning algorithm can on-line tune parameters of dilation and translation of fuzzy wavelet basis functions and hidden-to-output weights. Therefore, a robust control law is used to eliminate uncertainties including the inevitable approximation errors resulted from the finite number of fuzzy wavelet basis functions. The proposed controller requires no prior knowledge about the dynamics of the robot and no off-line learning phase. Moreover, both tracking performance and stability of the closed-loop robotic system can be guaranteed by Lyapunov theory. Finally, the effectiveness of the fuzzy wavelet network-based control approach is illustrated through comparative simulations on a six-link robot manipulator View full abstract»

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  • Logic-Based Fuzzy Neurocomputing With Unineurons

    Page(s): 860 - 873
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2500 KB) |  | HTML iconHTML  

    In this paper, we introduce a new category of logic neurons- unineurons that are based on the concept of uninorms. As uninorms form a certain generalization of the generic categories of fuzzy set operators such as t-norms and t-conorms, the proposed unineurons inherit their logic processing capabilities which make them flexible and logically appealing. We discuss several fundamental categories of uninorms (such as UNI_or, UNI_and, and alike). In particular, we focus on the interpretability of networks composed of unineurons leading to several categories of rules to be exploited in rule-based systems. The learning aspects of the unineurons are presented along with detailed optimization schemes. Experimental results tackle two categories of problems such as: (a) a logic approximation of fuzzy sets, and (b) a design of associations between information granules where the ensuing development schemes directly relate to the fundamentals of granular (fuzzy) modeling View full abstract»

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  • Stability Conditions for LMI-Based Fuzzy Control From Viewpoint of Membership Functions

    Page(s): 874 - 884
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (527 KB) |  | HTML iconHTML  

    In this paper, we investigate the stability conditions for linear matrix inequality (LMI)-based fuzzy control design. Especially, we focus on the dependence of the stability upon membership functions. In general, the membership functions in the rule bases of Takagi-Sugeno (T-S) fuzzy model and controllers are the same and restricted between 0 and 1. In contrast to this setting, we obtain some new results when different membership functions are considered and their values lying outside the interval of [0,1] are allowed. Applying Lyapunov equation and a convex hull of fuzzy subsystems, we first establish a relationship between the stable interval characteristic polynomial and a set of feasible LMIs. Then Kharitonov's theorem gives an insight for the solvability of stabilization problems using LMI-based design and, this leads that the membership functions have an influence on stability. On the other hand, the LMI condition leads to the well-known results for LMI-based fuzzy control design. We further indicate that the different LMI conditions arise due to the same or different membership functions and find their own applications on adaptive fuzzy control. Finally, if the unit interval constraint is removed, an LMI condition for global stability is obtained View full abstract»

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  • MembershipMap: Data Transformation Based on Granulation and Fuzzy Membership Aggregation

    Page(s): 885 - 896
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1218 KB) |  | HTML iconHTML  

    We propose a new data-driven transformation that facilitates many data mining, interpretation, and analysis tasks. Our approach, called MembershipMap, strives to granulate and extract the underlying subconcepts of each raw attribute. The orthogonal union of these subconcepts are then used to define a new membership space. The subconcept soft labels of each point in the original space determine the position of that point in the new space. Since subconcept labels are prone to uncertainty inherent in the original data and in the initial extraction process, a combination of labeling schemes that are based on different measures of uncertainty will be presented. In particular, we introduce the CrispMap, the FuzzyMap, and the PossibilisticMap. We outline the advantages and disadvantages of each mapping scheme, and we show that the three transformed spaces are complementary. We also show that in addition to improving the performance of clustering by taking advantage of the richer information content, the MembershipMap can be used as a flexible preprocessing tool to support such tasks as: sampling, data cleaning, and outlier detection View full abstract»

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  • A New Fuzzy Multidimensional Model

    Page(s): 897 - 912
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    As a result of the use of OLAP technology in new fields of knowledge and the merging of data from different sources, it has become necessary for models to support this technology. In this paper, we shall propose a new multidimensional model that can manage imprecision in both dimensions and facts and hide the complexity to the end user. The multidimensional structure is therefore able to model data imprecision resulting from the integration of data from different sources or even information from experts, which it does by means of fuzzy logic View full abstract»

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  • 2006 Index

    Page(s): 913 - 920
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  • IEEE Computational Intelligence Society Information

    Page(s): C3
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  • IEEE Transactions on Fuzzy Systems Information for authors

    Page(s): C4
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Aims & Scope

The IEEE Transactions on Fuzzy Systems (TFS) is published quarterly. TFS will consider papers that deal with the theory, design or an application of fuzzy systems ranging from hardware to software.

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Meet Our Editors

Editor-in-Chief
Chin-Teng Lin
National Chiao-Tung University