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Fuzzy Systems Conference Proceedings, 1999. FUZZ-IEEE '99. 1999 IEEE International

Date 22-25 Aug. 1999

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  • 1999 IEEE International Fuzzy Systems Conference Proceedings

    Publication Year: 1999, Page(s):0_2 - 1_11
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  • Adaptation, learning and evolution for intelligent systems

    Publication Year: 1999, Page(s):1203 - 1210 vol.3
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (573 KB)

    Intelligent systems are required in knowledge engineering, computer science, mechatronics and robotics. This paper discusses the machine (system) intelligence from the viewpoints of adaptation, learning and evolution of living things. Next, this paper introduces computational intelligence including neural network, fuzzy system and genetic algorithm. Finally, this paper describe the sensor fusion s... View full abstract»

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  • Fuzzy data analysis: challenges and perspectives

    Publication Year: 1999, Page(s):1211 - 1216 vol.3
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (517 KB)

    In meeting the challenges that resulted from the explosion of collected, stored, and transferred data, knowledge discovery in databases or data mining has emerged as a new research area. However, the approaches studied in this area have mainly been oriented at highly structured and precise data. In addition, the goal to obtain understandable results is often neglected. Therefore we suggest to conc... View full abstract»

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  • FARM: a data mining system for discovering fuzzy association rules

    Publication Year: 1999, Page(s):1217 - 1222 vol.3
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (427 KB)

    In this paper, we introduce a novel technique, called FARM, for mining fuzzy association rules. FARM employs linguistic terms to represent the revealed regularities and exceptions. The linguistic representation is especially useful when those rules discovered are presented to human experts for examination because of the affinity with the human knowledge representations. The definition of linguisti... View full abstract»

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  • Efficient fuzzy rule generation based on fuzzy decision tree for data mining

    Publication Year: 1999, Page(s):1223 - 1228 vol.3
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (457 KB)

    In this paper, we propose an efficient fuzzy rule generation algorithm based on fuzzy decision tree for data mining. We combine the comprehensibility of rules generated based on decision tree such as ID3 and C4.5 and the expressive power of fuzzy sets. Particularly, fuzzy rules allow us to effectively classify patterns of nonaxis-parallel decision boundaries, which are difficult to do using attrib... View full abstract»

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  • Regression analysis based on fuzzy evidence theory

    Publication Year: 1999, Page(s):1229 - 1234 vol.3
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (416 KB)

    We propose an approach to functional regression analysis based on fuzzy evidence theory. This method uses a training set for computing a fuzzy belief structure that quantifies different sorts of uncertainties, such as nonspecificity, discord in the output data, or low density around the input data. The method can use a very large class of output data, such as real, interval or fuzzy numbers, or, m... View full abstract»

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  • Granular correlation analysis in data mining

    Publication Year: 1999, Page(s):1235 - 1240 vol.3
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    We introduce and study the use of the concept of granular correlation. Granular correlation arises as a result of introducing fuzzy information granules and can be regarded as a generic vehicle of data mining. It is shown how an analysis of fuzzy granular correlation helps reveal and quantify relationships between variables in any task of data mining. This analysis sheds light on an important issu... View full abstract»

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  • Generation of approximation rules using information gain

    Publication Year: 1999, Page(s):1241 - 1245 vol.3
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    Suggests a method for generating approximation rules by the information gain used in the machine learning by decision tree. We studied that these rules are better than other approximation rules induced by using /spl chi//sup 2/ goodness of fittest or dependency of attributes in rough set theory by an experiment using neural network. View full abstract»

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  • Mining linguistic summaries of databases using based Lukasiewicz implication fuzzy functional dependency

    Publication Year: 1999, Page(s):1246 - 1250 vol.3
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (352 KB)

    Data and knowledge mining have been recognized by many researchers as a key research topic in database systems. We are concerned with mining linguistic summaries of a relational database. The discovery of these linguistic summaries is based on fuzzy functional dependency, using Lukasiewicz implication, and their derived sound properties. We present an algorithm which implements the discovery proce... View full abstract»

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  • A fuzzy approach to content-based image retrieval

    Publication Year: 1999, Page(s):1251 - 1260 vol.3
    Cited by:  Papers (6)  |  Patents (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (721 KB)

    Describes an approach to content-based image retrieval that can handle exemplar-based, graphical-sketch-based, as well as linguistic queries involving region (object) labels, attribute values, and spatial relations. Images are internally represented by fuzzy attributed relational graphs, where each node in the graph represents an image region, and each edge represents a relation between two image ... View full abstract»

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  • An improved robust fuzzy clustering algorithm

    Publication Year: 1999, Page(s):1261 - 1265 vol.3
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (344 KB)

    Fuzzy-c-means (FCM) and hard clustering algorithms are the most common tools for data partitioning. However, these clustering algorithms may fail completely in the presence of noise. We introduce a robust noise rejection clustering algorithm based on a combination of techniques that treat the FCM weak points with a traditional noise rejection algorithm. Unlike the traditional FCM, the proposed alg... View full abstract»

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  • Unsupervised feature selection using a fuzzy-genetic algorithm

    Publication Year: 1999, Page(s):1266 - 1269 vol.3
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (285 KB)

    Presents an unsupervised feature selection method using a fuzzy-genetic approach. The method minimizes a feature evaluation index which incorporates a weighted distance used to rank the importance of the individual features. In addition, a fuzzy membership function is employed to determine the degree of closeness for each pair of patterns which are used in the feature evaluation index. A genetic a... View full abstract»

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  • Supervised fuzzy clustering for rule extraction

    Publication Year: 1999, Page(s):1270 - 1274 vol.3
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (280 KB)

    The paper is concerned with the application of orthogonal transforms and fuzzy clustering to the extraction of fuzzy rules from data. It is proposed to use the orthogonal least squares method to supervise the progress of the fuzzy clustering algorithm and remove clusters of less importance with respect to fitting the data. Clustering takes place in the product space of systems in and outputs, and ... View full abstract»

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  • A fuzzy approach to statistical models in speech and speaker recognition

    Publication Year: 1999, Page(s):1275 - 1280 vol.3
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (368 KB)

    A unified fuzzy approach to statistical models for speech and speaker recognition is presented. Since the expectation-maximisation (EM) algorithm is a powerful learning method for maximising the likelihood of the observed data in the presence of hidden variables, the fuzzy EM algorithm based on the fuzzy c-means algorithm is thereby established. From this fuzzy EM algorithm, the fuzzy algorithms f... View full abstract»

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  • A fuzzy relative of the k-medoids algorithm with application to web document and snippet clustering

    Publication Year: 1999, Page(s):1281 - 1286 vol.3
    Cited by:  Papers (30)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (436 KB)

    This paper presents new algorithms (fuzzy e-methods (FCMdd) and fuzzy c trimmed medoids (FCTMdd)) for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total dissimilarity within each cluster is minimized. A comparison of FCMdd with the relational fuzzy c-means algorithm shows that FCMdd i... View full abstract»

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  • New design for an online adaptive fuzzy controller

    Publication Year: 1999, Page(s):1287 - 1292 vol.3
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (415 KB)

    Substantial developments in optimizing control methods for different purposes have been made in the field of fuzzy control in recent years. However, most of them are based on a known system model, whereas in practice such models are not usually available due to the complexity of the plant to be controlled. In this paper, an adaptive fuzzy controller optimizes the altitude control of a helicopter, ... View full abstract»

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  • Robust indirect adaptive fuzzy control

    Publication Year: 1999, Page(s):1293 - 1298 vol.3
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    This paper proposes a robust indirect adaptive fuzzy controller that can be applied for tracking control of a class of uncertain nonlinear SISO systems. It is shown that, in the presence of the perturbations such as external disturbances and approximation error of the fuzzy logic system, boundedness of all the signals in the system is ensured, while under the assumption of no perturbations, the st... View full abstract»

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  • Adaptive fuzzy logic controller blending the concepts of linguistic hedges and genetic algorithms

    Publication Year: 1999, Page(s):1299 - 1304 vol.3
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (410 KB)

    This paper describes a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. It is based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators adjust the shape of the system membership functions dynamically, and speed up the controller targeting its goa... View full abstract»

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  • Robust adaptive control using fuzzy-neural controller

    Publication Year: 1999, Page(s):1305 - 1308 vol.3
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (264 KB)

    This paper proposes an adaptive fuzzy-neural control scheme that yields robust trajectory tracking in the presence of parametric and unstructured uncertainty. The uncertainties include bounded disturbances, dynamic-parametric changes as well as unmodeled dynamics which is dependent on state variables. The proposed method employs fuzzy-neural controlled to compensate for uncertain nonlinearity of d... View full abstract»

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  • Further results on adaptive control of a class of nonlinear systems with fuzzy logic

    Publication Year: 1999, Page(s):1309 - 1314 vol.3
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (367 KB)

    In a recent work of Su et al. (1994), a robust adaptive fuzzy controller was proposed for a class of nonlinear systems in the presence of dominant uncertain nonlinearities. However, in the scheme only the parameters (weights), which appear linearly in the radial basis function expansion, were tuned. To further improve the tracking performance of the control systems, a novel adaptive fuzzy controll... View full abstract»

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  • Control of dead-zone process using online parameter-adaptive fuzzy logic control

    Publication Year: 1999, Page(s):1315 - 1320 vol.3
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (354 KB)

    New online parameter-adaptive fuzzy logic control is presented and applied to tracking control of a dead-zone process. To implement the adaptive fuzzy logic control, the normalized state distance from the origin (NSDO) and two functions using NSDO are defined. Using the one of these functions, the fuzzy sets are modified to logarithmic scale in accordance with a weighting coefficient. With this sc... View full abstract»

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  • The unbearable richness of fuzzy relational calculus for knowledge processing

    Publication Year: 1999
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    Summary form only given. The concept of a relation is one of the most fundamental notions in pure as well as in applied sciences. The fuzzification of this fundamental notion, i.e. instead of dealing with connected or unconnected objects one considers objects that are linked in some degree, has thoroughly enriched the applicability of this notion. Due to the notions of after and foreset introduced... View full abstract»

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  • Differentiation of the Choquet integral of a nonnegative measurable function

    Publication Year: 1999, Page(s):1322 - 1327 vol.3
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (285 KB)

    Differentiation of the Choquet integral of a nonnegative measurable function taken with respect to a fuzzy measure on fuzzy measure space is proposed. First, the real interval limited Choquet integral is defined for a preparation, then the upper differential coefficient, the lower differential coefficient, the differential coefficient and the derived function of the Choquet integral along the rang... View full abstract»

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  • A unified view of ranking techniques for fuzzy numbers

    Publication Year: 1999, Page(s):1328 - 1333 vol.3
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (502 KB)

    The paper provides a structured discussion of the numerous approaches which have been proposed for ranking fuzzy numbers. Emphasis is put on principles rather than on technical details or on computational issues for the sake of brevity. This structured presentation provides a basis for comparing fuzzy ranking methods. View full abstract»

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  • A proposal for a new fuzzy probability distribution function

    Publication Year: 1999, Page(s):1334 - 1339 vol.3
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (287 KB)

    This paper proposes a new fuzzy probability distribution function containing fuzzy numbers as its parameters. Concretely, the normal distribution which is widely used in the field of stochastic processes and statistics is developed into the fuzzy probability distribution function defined by the fuzzy numbers of the mean and the standard deviation respectively. The following points are studied: 1) ... View full abstract»

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