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Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on

Date 25-28 May 2003

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  • Testing continuous t-norm called Lukasiewicz algebra with different means in classification

    Publication Year: 2003 , Page(s): 808 - 813 vol.2
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (459 KB) |  | HTML iconHTML  

    In this paper, we have done new similarity measures from a continuous t-norm by implementing it in different mean measures. For the implementation, we use a Minkowsky metric based on Lukasiewicz algebra. We test these new similarities in both the generalised and normal form of Lukasiewicz algebra with weight optimisation. The mean measures examined here are arithmetic, geometric and harmonic means. We show that the magnitude order of the similarities are SHN 1, x2>≥SGN 1, x2>≥SAN 1, x2>. Secondly, we show that the use of different means is highly recommendable in some cases. View full abstract»

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  • Soft computing and fractal theory for industrial applications

    Publication Year: 2003 , Page(s): 1492
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  • Type-2 fuzzy logic made simple

    Publication Year: 2003 , Page(s): 1493
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  • Granular fuzzy Web intelligence techniques for profitable data mining

    Publication Year: 2003 , Page(s): 1462 - 1464 vol.2
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (320 KB) |  | HTML iconHTML  

    Data mining has a lot of e-commerce applications. The key problem is how to find useful hidden patterns for better business applications. For these problems, granular fuzzy Web intelligence techniques are used to implement the granular fuzzy Web data mining system for available historical data of the credit company customers. Fuzzy computing and granular computing are used to design the Web fuzzy-interval data mining system that can do fuzzy-interval data clustering under uncertainty. View full abstract»

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  • A new genetic algorithm for nonlinear multiregressions based on generalized Choquet integrals

    Publication Year: 2003 , Page(s): 819 - 821 vol.2
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (257 KB) |  | HTML iconHTML  

    This paper gives a new genetic algorithm for nonlinear multiregression based on generalized Choquet integrals with respect to signed fuzzy measures. Unlike the previous work where the values of the signed fuzzy measure are determined by random search in a genetic algorithm with other regression coefficients together; in this new algorithm, they are determined algebraically and, therefore, its complexity is much lower than before. View full abstract»

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  • Traffic engineering with MPLS using fuzzy logic for application in IP networks

    Publication Year: 2003 , Page(s): 1146 - 1151 vol.2
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (464 KB) |  | HTML iconHTML  

    One of the great challenges nowadays when managing IP networks is to guarantee proper Quality of Service, using network infrastructure on optimized way. One of the proposed solutions is traffic engineering with MPLS. However, the characterization of the demands and of the network state are difficult tasks, considering that the demands and the data traffic are random, consequently, the network state changes dynamically and in a random way. In this work we propose a connection admission controller that uses fuzzy logic based on linguistic rules to treat the inaccurate information in IP over MPLS networks with the purpose of offering Quality of Service to the users. In accordance with the simulation results, we concluded that the use of fuzzy logic allows a large flexibility in the connection admission process and the possibility to include more network and traffic information when making a decision without increasing considerably the controller complexity. View full abstract»

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  • Computational intelligence for robotic systems

    Publication Year: 2003 , Page(s): 1495
    Cited by:  Papers (1)
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  • Obtaining the relevant colors of an image through stability-based fuzzy color histograms

    Publication Year: 2003 , Page(s): 914 - 919 vol.2
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (593 KB) |  | HTML iconHTML  

    In a previous work, we introduced a fuzzy-based color image segmentation method for detecting the "relevant colors" of an image, founded on color histogram analysis. The present paper outlines the whole process and introduces the construction of a new fuzzy paradigm-based color histogram, which uses color components' stability degrees to modify a set of histogram positions according to the uncertainty of the pixel color components. The processing of such histogram provides a group of fuzzy sets that fit the number and domain of image's relevant colors better than the processing of the traditional color histogram. Moreover, the image segmentation results are more similar to the human visual perception of the scene. View full abstract»

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  • An extended Kalman filter (EKF) approach on fuzzy system optimization problem

    Publication Year: 2003 , Page(s): 1465 - 1470 vol.2
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (432 KB) |  | HTML iconHTML  

    Optimizing the membership functions of a fuzzy system can be viewed as a system identification problem for a nonlinear dynamic system. Basically, we can view the optimization of fuzzy membership functions as a weighted least-squares minimization problem, where the error vector is the difference between the fuzzy system outputs and the target values for those outputs. The extended Kalman filter algorithm is a good choice to solve this system identification problem, not only because it is a derivative-based algorithm that is suitable to solve the weighted least-squares minimization problem, but also because of its appealing predictor-corrector feature for nonlinear system model. In this paper, we present an extended Kalman filter approach to optimize the membership functions of the inputs and outputs of the fuzzy controller. The effect of the measurement noise covariance R on the convergence of the fuzzy controller is also investigated. Experimental results show that the optimized fuzzy controller achieves significant improvement on performance. In addition, the smaller the measurement noise covariance R is, the faster the optimized fuzzy controller would converge. View full abstract»

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  • Recognition of handwritten characters using modified fuzzy hyperline segment neural network

    Publication Year: 2003 , Page(s): 1418 - 1422 vol.2
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (331 KB) |  | HTML iconHTML  

    In this paper membership function of fuzzy hyperline segment neural network (FHLSNN) proposed by U.V. Kulkarni and T.R. Sontakke is modified to maintain convexity. The modified membership function is found superior than the function defined by them, which gives relatively lower values to the patterns which are falling close to the hyperline segment (HLS) but far from two end points of HLS. The performance of modified fuzzy hyperline segment neural network (MFHLSNN) is tested with the two splits of FISHER IRIS data and is found superior than FHLSNN. The modified neural network is also found superior than the general fuzzy min-max neural network (GFMM), proposed by Bogdan Gabrys and Andrzej Bargiela, and general fuzzy hypersphere neural network (GFHSNN), proposed by U.V. Kulkarni, D.D. Doye and T.R. Sontakke. View full abstract»

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  • Direct adaptive fuzzy control with state observer for a class of nonlinear systems

    Publication Year: 2003 , Page(s): 1338 - 1343 vol.2
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (350 KB) |  | HTML iconHTML  

    In this paper, we develop an observer-based direct adaptive fuzzy logic controller for a certain class of nonlinear systems. The complete states of the nonlinear system are not assumed to be available for measurement. By using an observer-based output feedback control law and adaptive law, the free parameters of the direct adaptive controller can be tuned on line based on the Lyapunov synthesis approach. A supervisory controller is designed to guarantee the boundedness of the state vector. Moreover, a robust control term is adapted to compensated for the approximation error and output disturbances of the system. The overall adaptive scheme guarantees that all signals involved are bounded. Simulation results illustrates the performance of the proposed algorithm. View full abstract»

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  • Fuzzy clustering for intrusion detection

    Publication Year: 2003 , Page(s): 1274 - 1278 vol.2
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (463 KB) |  | HTML iconHTML  

    The newly formed Department of Homeland Security has been mandated to reduce America's vulnerability to terrorism. In addition to being charged with physical protection, this newly formed department is also responsible for protecting the nation's critical infrastructure. Protecting computer systems from intrusions is an important aspect of securing the nation's infrastructure. We are exploring how fuzzy data mining and concepts introduced by the semantic Web can operate in synergy to perform distributed intrusion detection. The underlying premise of our intrusion detection model is to describe attacks as instances of an ontology using a semantically rich language, reason over them and subsequently classify them as instances of an attack of a specific type. However, before an abnormality can be specified as an instance of the ontology, it first needs to be detected. Hence, our intrusion detection model is two phased, where the first phase uses data mining techniques to analyze low level data streams that capture process, system and network states and to detect anomalous behavior. The second phase reasons over instances of anomalous behavior specified according to our ontology. This paper focuses on the initial phase of our model: outlier detection within low level data streams. Accordingly, we present the preliminary results of the use of fuzzy clustering to detect anomalies within low level kernel data streams. View full abstract»

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  • Selection of structure preserving features with neural networks

    Publication Year: 2003 , Page(s): 822 - 827 vol.2
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    We propose a neural network for selection of features. Given a data set X in p dimension, the network can select the best subset of q (q View full abstract»

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  • Combined uncertainty model for best wavelet selection

    Publication Year: 2003 , Page(s): 1195 - 1199 vol.2
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (382 KB) |  | HTML iconHTML  

    This paper discusses the use of combined uncertainty methods in the computation of wavelets that best represent horse gait signals. Combined uncertainty computes a composite of two types of uncertainties, fuzzy and probabilistic. First, we introduce fuzzy uncertainty properties and classes. Next, the gait analysis problem is discussed in the context of correctly classifying wavelet-transformed sound gait from lame horse gait signals. Continuous wavelets are selected using generalized information theory-related concepts that are enhanced through the application of uncertainty management models. Our experimental results show that models developed by maximizing combined uncertainty produce better results, in terms of neural network correct classification percentage, compared to those computed using only fuzzy uncertainty. View full abstract»

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  • Modelling of river discharges using neural networks derived from support vector regression

    Publication Year: 2003 , Page(s): 1321 - 1326 vol.2
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (416 KB) |  | HTML iconHTML  

    Neural networks are often used to model complex and nonlinear systems, as they can approximate nonlinear systems with arbitrary accuracy and can be trained from data. Amongst the neural networks, Associative Memory Networks (AMNs) are often used, since they are less computation intensive, and yet good generalization results can be obtained. However, this can only be achieved if the structure of the AMNs is suitably chosen. An approach to choose the structure of the AMNs is to use the Support Vectors (SVs) obtained from the Support Vector Machines. The SVs are obtained from a constrained optimization for a given data set and an error bound. For convenience, this class of AMNs is referred to as the Support Vector Neural Networks (SVNNs). In this paper, the modelling of river discharges with rainfall as input using the SVNN is presented, from which the nonlinear dynamic relationship between rainfall and river discharges is obtained. The prediction of river discharges from the SVNN can give early warning of severe river discharges when there are heavy rainfalls. View full abstract»

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  • An inexact inferencing strategy for spatial objects with determined and indeterminate boundaries

    Publication Year: 2003 , Page(s): 778 - 783 vol.2
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (452 KB) |  | HTML iconHTML  

    For many years, spatial querying has been of interest for the researchers in the GIS community. Any successful implementation and long-term viability of the GIS technology depends on the issue of accuracy of spatial queries. In order to improve the accuracy and quality of spatial querying, the problems associated with the areas of fuzziness and uncertainty need to be addressed. There has been a strong demand to provide approaches that deal with inaccuracy and uncertainty in GIS. In this paper, we develop an approach that can perform fuzzy spatial querying under uncertainty. An inexact inferencing strategy for objects with determined and indeterminate boundaries is investigates using type-2 fuzzy set theory. View full abstract»

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  • Fuzzy feature extraction and visualization for intrusion detection

    Publication Year: 2003 , Page(s): 1249 - 1254 vol.2
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (492 KB) |  | HTML iconHTML  

    The Fuzzy Intrusion Recognition Engine (FIRE) is a network intrusion detection system that uses fuzzy systems to assess malicious activity against computer networks. A key part of an intrusion detection system is the selection of key features that can characterize the state of the network. This work uses interactive data visualization to analyze the features of several different intrusion detection scenarios using the DARPA Lincoln Labs test data. Visualizing the data helps to characterize which features are key for identifying intrusions and if they can be characterized as fuzzy sets or by Boolean variables. These inputs can then be input into a fuzzy cognitive map that serves to fuse the inputs to detect more complex attacks. View full abstract»

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  • Neuro-fuzzy classification of surface form deviations

    Publication Year: 2003 , Page(s): 902 - 907 vol.2
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (484 KB) |  | HTML iconHTML  

    Today the method for surface quality analysis of exterior car body panels is still characterized by manual detection of local form deviations and evaluation by experts. The new approach presented in this paper is based on 3-D image processing. A major step in this process is the classification of the different kinds of surface form deviations. For this purpose, we used neuro-fuzzy classification and other soft computing techniques and compared the performance of the different approaches. Although the dataset was rather small, high-dimensional and unbalanced, we achieved promising results with regard to classification accuracies and interpretability of rule bases. View full abstract»

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  • Classification of anomalous traces of privileged and parallel programs by neural networks

    Publication Year: 2003 , Page(s): 1225 - 1230 vol.2
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (492 KB) |  | HTML iconHTML  

    The focus of intrusion detection has recently shifted from user-based and connection-based to process-based intrusion detection. Substantial research has been done in the analysis of system call logs using different methods including neural networks. Detection is based on the classification of short sequences as anomalous or normal. The classification of interest, however, is the status of the program trace, not just the short sequences. In this paper we report the results of a comparative study of three different methods for on-line classification of program traces based detection of anomalies in sequences of system calls by neural networks. These results demonstrate that methods that use information about the locality of anomalies are more effective than those that only look at the number of anomalies. View full abstract»

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  • An interpretation of discrete Choquet integrals in morphological image processing

    Publication Year: 2003 , Page(s): 1291 - 1295 vol.2
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    Mathematical morphology has been widely used for shape analysis and feature extraction in digital images. Morphological filters are based on morphological transformations of signals by crisp sets. Their sensitivity to noise led researchers define morphological operations on fuzzy sets. Choquet integrals provide another means of generalizing binary morphological operators. They operate on crisp sets. But, they are referred to as fuzzy integrals because they integrate a real function with respect to a fuzzy measure. An interpretation of a fuzzy measure in terms of fuzzy fitting has led us to generalize gray-scale morphological operators based on Choquet integrals. These operators are called Choquet Morphological Operators (CMO). We used them for domain learning, feature selection, information fusion and, feature extraction. CMOs have been shown to provide better results than conventional morphological operators. In this paper, we give a detailed discussion of the fuzzy fitting concept that leads us to generalizing gray-scale morphological operators using Choquet integrals. View full abstract»

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  • A fuzzy logic CBIR system

    Publication Year: 2003 , Page(s): 1171 - 1176 vol.2
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (555 KB) |  | HTML iconHTML  

    A fuzzy logic framework is proposed to alleviate two problems in traditional CBIR systems, including the semantic gap and the perception subjectivity. The proposed framework consists of two major parts, including (1) model construction and (2) query comparison. In the model construction part, fuzzy linguistic terms with associated fuzzy membership functions are automatically generated through an unsupervised fuzzy clustering algorithm. The linguistic terms provide a nature way of expressing user's concepts, and the membership functions characterize the mapping between image features and human visual concepts. We also define the syntax and semantics rules of a query description language to unify the query expression of textual descriptions, visual examples, and relevance feedbacks. In the query comparison part, a similarity function is inferred based on user's feedbacks to measure the similarity between the query and each image in the database. The user's preference is also captured and retained in his/her own profile to achieve personalization. Our work provides a unified and comprehensive framework for incorporation a fuzzy approach into CBIR systems. To verify our CBIR framework, we select Tamura features to describe and retrieve texture images. Experimental results show that the proposed framework is indeed effective to alleviate the semantic gap and the perception subjectivity problems. View full abstract»

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  • Fuzzy logic in collective robotic search

    Publication Year: 2003 , Page(s): 1471 - 1475 vol.2
    Cited by:  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (355 KB) |  | HTML iconHTML  

    One important application of mobile robots is searching a geographical region to locate the origin of a specific sensible phenomenon. We first propose a fuzzy logic approach using a decision table. A novel fuzzy rule based was designed. And then a fuzzy search strategy is adopted by utilizing the three tier centers of mass coordination. Experimental results show that fuzzy logic algorithm is an efficient approach for the collective robots to locate the target source. In addition, noise and the position of the target affect the searching result. View full abstract»

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  • An intelligent Web recommendation engine based on fuzzy approximate reasoning

    Publication Year: 2003 , Page(s): 1116 - 1121 vol.2
    Cited by:  Papers (6)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (598 KB) |  | HTML iconHTML  

    Intelligent Web personalization aims at adapting a user's interaction with the Web information space based on information gathered about the user. A complete automated Web personalization system is generally based on Web usage mining to discover useful knowledge about user access patterns, followed by a recommendation system to act on this knowledge in order to respond to the users' individual interest, in a manner transparent to the user, and while protecting the user's privacy and anonymity. The flow of information in a Web personalization system can be prone to significant amounts of error and uncertainty. This uncertainty pervades all stages from the user's Web navigation patterns to the final recommendations, including the intermediate stages of logging Web usage, preprocessing and segmenting Web log data into Web user sessions, clustering these sessions, and computing Web user profiles from these clusters. Fuzzy approximate reasoning can offer a general framework for the, recommendation process. It is this framework that is investigated in this paper. This paper presents a simple, intuitive, and fast approach to provide dynamic predictions in the Web navigation space. Real Web usage data is used as a simulation testbed for the fuzzy approximate reasoning based recommendation system. View full abstract»

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  • Fuzzy flow-shop scheduling models based on credibility measure

    Publication Year: 2003 , Page(s): 1423 - 1427 vol.2
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (370 KB) |  | HTML iconHTML  

    The credibility measure of fuzzy events is a relatively new concept related to fuzzy variable. This paper aims at demonstrating how this concept can be used for managing fuzzy scheduling on flow-shop problems. Three types of fuzzy flow-shop scheduling models are presented. A hybrid intelligent algorithm is then designed to solve the proposed fuzzy flow-shop scheduling models. Computation experiments are provided to illustrate its effectiveness. View full abstract»

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  • Fuzzy co-clustering of documents and keywords

    Publication Year: 2003 , Page(s): 772 - 777 vol.2
    Cited by:  Papers (19)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (445 KB) |  | HTML iconHTML  

    Conventional clustering algorithms such as K-means and SAHN (also known as AHC) have been well studied and used in the information retrieval community for clustering text documents. More recently, efforts have been made to cluster documents and words simultaneously. The FCCM algorithm due to Oh et al. is a fuzzy clustering algorithm that maximizes the co-occurrence of categorical attributes (keywords) and the individual patterns (documents) in clusters. However, this algorithm poses certain problems when the number of documents or the number of words is very large. In this paper, we modify the FCCM algorithm so that it can be used to cluster large text corpora. Our experiments show that the modified algorithm is scalable and produces meaningful clusters. We also show the relation between FCCM and the Spherical K-Means (SKM) algorithm and introduce the Spherical Fuzzy c-Means (SFCM) algorithm. View full abstract»

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