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Fuzzy Systems, IEEE Transactions on

Issue 2 • Date April 2003

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Displaying Results 1 - 12 of 12
  • Comment on "Optimal fuzzy controller design: local concept approach" [with reply]

    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (192 KB)  

    This paper points out some errors existing in the aforementioned paper. Unfortunately, it seems that no immediate correction can be done. The authors reply is provided. View full abstract»

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  • Modeling proportional membership in fuzzy clustering

    Page(s): 173 - 186
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (878 KB) |  | HTML iconHTML  

    To provide feedback from a cluster structure to the data from which it has been determined, we propose a framework for mining typological structures based on a fuzzy clustering model of how the data are generated from a cluster structure. To relate data entities to cluster prototypes, we assume that the observed entities share parts of the prototypes in such a way that the membership of an entity to a cluster expresses the proportion of the cluster's prototype reflected in the entity (proportional membership). In the generic version of the model, any entity may independently relate to any prototype, which is similar to the assumption underlying the fuzzy c-means criterion. The model is referred to as fuzzy clustering with proportional membership (FCPM). Several versions of the model relaxing the generic assumptions are presented and alternating minimization techniques for them are developed. The results of experimental studies of FCPM versions and the fuzzy c-means algorithm are presented and discussed, especially addressing the issues of fitting the underlying clustering model. An example is given with data in the medical field in which our approach is shown to suit better than more conventional methods. View full abstract»

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  • Fast accurate fuzzy clustering through data reduction

    Page(s): 262 - 270
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (480 KB) |  | HTML iconHTML  

    Clustering is a useful approach in image segmentation, data mining, and other pattern recognition problems for which unlabeled data exist. Fuzzy clustering using fuzzy c-means or variants of it can provide a data partition that is both better and more meaningful than hard clustering approaches. The clustering process can be quite slow when there are many objects or patterns to be clustered. This paper discusses the algorithm brFCM, which is able to reduce the number of distinct patterns which must be clustered without adversely affecting the partition quality. The reduction is done by aggregating similar examples and then using a weighted exemplar in the clustering process. The reduction in the amount of clustering data allows a partition of the data to be produced faster. The algorithm is applied to the problem of segmenting 32 magnetic resonance images into different tissue types and the problem of segmenting 172 infrared images into trees, grass and target. Average speed-ups of as much as 59-290 times a traditional implementation of fuzzy c-means were obtained using brFCM, while producing partitions that are equivalent to those produced by fuzzy c-means. View full abstract»

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  • Fuzzy association rules: general model and applications

    Page(s): 214 - 225
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (717 KB) |  | HTML iconHTML  

    The theory of fuzzy sets has been recognized as a suitable tool to model several kinds of patterns that can hold in data. In this paper, we are concerned with the development of a general model to discover association rules among items in a (crisp) set of fuzzy transactions. This general model can be particularized in several ways; each particular instance corresponds to a certain kind of pattern and/or repository of data. We describe some applications of this scheme, paying special attention to the discovery of fuzzy association rules in relational databases. View full abstract»

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  • Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators

    Page(s): 249 - 261
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (811 KB)  

    A new robust neuro-fuzzy controller for autonomous and intelligent robot manipulators in dynamic and partially known environments containing moving obstacles is presented. The navigation is based on a fuzzy technique for the idea of artificial potential fields (APFs) using analytic harmonic functions. Unlike the fuzzy technique, the development of APFs is computationally intensive. A computationally efficient processing scheme for fuzzy navigation to reasoning about obstacle avoidance using APF is described, namely, the intelligent dynamic motion planning. An integration of a robust controller and a modified Elman neural networks (MENNs) approximation-based computed-torque controller is proposed to deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The MENN weights are tuned online, with no off-line learning phase required. The stability of the overall closed-loop system, composed by the nonlinear robot dynamics and the robust neuro-fuzzy controller, is guaranteed by the Lyapunov theory. The purpose of the robust neuro-fuzzy controller is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems. View full abstract»

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  • Smart shopper: an agent-based web-mining approach to Internet shopping

    Page(s): 226 - 237
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (723 KB) |  | HTML iconHTML  

    This paper presents an agent-based Web-mining approach to Internet shopping. We propose a fuzzy neural network to tackle the uncertainties in practical shopping activities, such as consumer preferences, product specification, product selection, price negotiation, purchase, delivery, after-sales service and evaluation. The fuzzy neural network provides an automatic and autonomous product classification and selection scheme to support fuzzy decision making by integrating fuzzy logic technology and the backpropagation feed forward neural network. In addition, a new visual data model is introduced to overcome the limitations of the current Web browsers that lack flexibility for customers to view products from different perspectives. Such a model also extends the conventional data warehouse schema to deal with intensive data volumes and complex transformations with a high degree of flexibility for multiperspective visualization and morphing capability in an interactive environment. Furthermore, an agent development tool named "Aglet" is used as a programming framework for system implementation. The integration of dynamic object visualization, interactive user interface and data mining decision support provides an effective technique to close the gap between the "real world" and the "cyber world" from a business perspective. The experimental results demonstrate the feasibility of the proposed approach for Web-based business transactions. View full abstract»

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  • H state-feedback controller design for discrete-time fuzzy systems using fuzzy weighting-dependent Lyapunov functions

    Page(s): 271 - 278
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (616 KB) |  | HTML iconHTML  

    For discrete-time Takagi-Sugeno (TS) fuzzy systems, we propose an H state-feedback fuzzy controller associated with a fuzzy weighting-dependent Lyapunov function. The controller, which is designed via parameterized linear matrix inequalities (PLMIs), employs not only the current-time but also the one-step-past information on the time-varying fuzzy weighting functions. Appropriately selecting the structures of variables in the PLMIs allows us to find an LMI formulation as a special case. View full abstract»

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  • Learning possibilistic graphical models from data

    Page(s): 159 - 172
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (766 KB)  

    Graphical models - especially probabilistic networks like Bayes networks and Markov networks - are very popular to make reasoning in high-dimensional domains feasible. Since constructing them manually can be tedious and time consuming, a large part of recent research has been devoted to learning them from data. However, if the dataset to learn from contains imprecise information in the form of sets of alternatives instead of precise values, this learning task can pose unpleasant problems. In this paper, we survey an approach to cope with these problems, which is not based on probability theory as the more common approaches like, e.g., expectation maximization, but uses the possibility theory as the underlying calculus of a graphical model. We provide semantic foundations of possibilistic graphical models, explain the rationale of possibilistic decomposition as well as the graphical representation of decompositions of possibility distributions and finally discuss the main approaches to learn possibilistic graphical models from data. View full abstract»

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  • OFFSS: optimal fuzzy-valued feature subset selection

    Page(s): 202 - 213
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (678 KB) |  | HTML iconHTML  

    Feature subset selection is a well-known pattern recognition problem, which aims to reduce the number of features used in classification or recognition. This reduction is expected to improve the performance of classification algorithms in terms of speed, accuracy and simplicity. Most existing feature selection investigations focus on the case that the feature values are real or nominal, very little research is found to address the fuzzy-valued feature subset selection and its computational complexity. This paper focuses on a problem called optimal fuzzy-valued feature subset selection (OFFSS), in which the quality-measure of a subset of features is defined by both the overall overlapping degree between two classes of examples and the size of feature subset. The main contributions of this paper are that: 1) the concept of fuzzy extension matrix is introduced; 2) the computational complexity of OFFSS is proved to be NP-hard; 3) a simple but powerful heuristic algorithm for OFFSS is given; and 4) the feasibility and simplicity of the proposed algorithm are demonstrated by applications of OFFSS to fuzzy decision tree induction and by comparisons with three different feature selection techniques developed recently. View full abstract»

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  • Classification by nonlinear integral projections

    Page(s): 187 - 201
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1513 KB)  

    A new method based on nonlinear integral projections for classification is presented. The contribution rate of each combination of the feature attributes, including each singleton, toward the classification is represented by a fuzzy measure. The nonadditivity of the fuzzy measure reflects the interactions among the feature attributes. The weighted Choquet integral with respect to the fuzzy measure serves as an aggregation tool to project the feature space onto a real axis optimally according to an error criterion, and the classifying attribute is properly numerical analysed on the axis simultaneously making the classification simple. To implement the classification, we need to determine the unknown parameters, the values of fuzzy measure and the weight function. This can be done by running an adaptive genetic algorithm on the given training data. The new classifier is tested by recovering the preset parameters from a set of artificial training data generated from these parameters. It also performs well on several real-world data sets. Beyond discriminating classes, this method can also learn the scaling requirements and the respective importance indexes of the feature attributes as well as the relationships among them. A comprehensive discussion on the semantic and geometric meanings of the parameters is given. Moreover, we show how these parameters' values can be used for short-listing important feature attributes to reduce the complexity (dimensions) of the classification problem. Our new method also compares favorably with other methods on some well-known real-world benchmarks. View full abstract»

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  • Mining fuzzy association rules in a bank-account database

    Page(s): 238 - 248
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (632 KB) |  | HTML iconHTML  

    This paper describes how we applied a fuzzy technique to a data-mining task involving a large database that was provided by an international bank with offices in Hong Kong. The database contains the demographic data of over 320,000 customers and their banking transactions, which were collected over a six-month period. By mining the database, the bank would like to be able to discover interesting patterns in the data. The bank expected that the hidden patterns would reveal different characteristics about different customers so that they could better serve and retain them. To help the bank achieve its goal, we developed a fuzzy technique, called fuzzy association rule mining II (FARM II). FARM II is able to handle both relational and transactional data. It can also handle fuzzy data. The former type of data allows FARM II to discover multidimensional association rules, whereas the latter data allows some of the patterns to be more easily revealed and expressed. To effectively uncover the hidden associations in the bank-account database, FARM II performs several steps which are described in detail in this paper. With FARM II, the bank discovered that they had identified some interesting characteristics about the customers who had once used the bank's loan services but then decided later to cease using them. The bank translated what they discovered into actionable items by offering some incentives to retain their existing customers. View full abstract»

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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.

Full Aims & Scope

Meet Our Editors

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