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Fuzzy Systems, IEEE Transactions on

Issue 1 • Date Feb 2001

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Displaying Results 1 - 22 of 22
  • Guest editorial - Foreword to the special issue on recognition technology

    Publication Year: 2001 , Page(s): 1 - 2
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    Freely Available from IEEE
  • Foreword - Recognition technology and fuzzy logic

    Publication Year: 2001 , Page(s): 3 - 4
    Cited by:  Papers (2)
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    Freely Available from IEEE
  • Comments on computing extreme values in 'stability issues on Takagi-Sugeno fuzzy model-parametric approach" [with reply]

    Publication Year: 2001 , Page(s): 221 - 223
    Cited by:  Papers (3)
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    The commenters analyze the results published in the above paper by Lo and Chen (ibid. vol.17 (1999)) which concern a new method of stability analysis of Takagi-Sugeno fuzzy systems, and amend the comments given by Johansen et al. (ibid. vol.8 (2000)). It is shown that the computational procedure presented by Lo-Chen is not valid for fuzzy systems where the number of rules is greater than three. In reply, Lo-Chen have modified the original claim and patched up the error to generate a sufficient effect provided that an appropriate switching is taken into account; the switching criterion is not overly restrictive and the nature of fuzzy systems permits such practices. They also point out that a computational procedure for multidimensional cases and a routine construction is readily available in the Matlab package. View full abstract»

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  • Authors' reply

    Publication Year: 2001 , Page(s): 222 - 223
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    First Page of the Article
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  • Hybrid control using recurrent fuzzy neural network for linear induction motor servo drive

    Publication Year: 2001 , Page(s): 102 - 115
    Cited by:  Papers (26)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (320 KB)  

    A hybrid control system using a recurrent fuzzy neural network (RFNN) is proposed to control a linear induction motor (LIM) servo drive. First, feedback linearization theory is used to decouple the thrust force and the flux amplitude of the LIM. Then, a hybrid control system is proposed to control the mover of the LIM for periodic motion. In the hybrid control system, the RFNN controller is the main tracking controller, which is used to mimic a perfect control law, and the compensated controller is proposed to compensate the difference between the perfect control law and the RFNN controller. Moreover, an online parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method is proposed to increase the learning capability of the RFNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results. Furthermore, the advantages of the proposed control system are indicated in comparison with the sliding mode control system View full abstract»

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  • Secure communications of chaotic systems with robust performance via fuzzy observer-based design

    Publication Year: 2001 , Page(s): 212 - 220
    Cited by:  Papers (13)
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    This paper presents a systematic design methodology for fuzzy observer-based secure communications of chaotic systems with guaranteed robust performance. The Takagi-Sugeno fuzzy models are given to exactly represent chaotic systems. Then, the general fuzzy model of many well-known chaotic systems is constructed with only one premise variable in fuzzy rules and the same premise variable in the system output. Based on this general model, the fuzzy observer of chaotic system is given and leads the stability condition of a linear-matrix inequality problem. When taking the fuzzy observer-based design to applications on secure communications, the robust performance is presented by simultaneously considering the effects of parameter mismatch and external disturbances. Then, the error of the recovered message is stated in an H criterion. In addition, if the communication system is free of external disturbances, the asymptotic recovering of the message is obtained in the same framework. The main results also hold for applications on chaotic synchronization. Numerical simulations illustrate that this proposed scheme yields robust performance View full abstract»

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  • Avoiding exponential parameter growth in fuzzy systems

    Publication Year: 2001 , Page(s): 194 - 199
    Cited by:  Papers (28)
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    For standard fuzzy systems where the input membership functions are defined on a grid on the input space, and all possible combinations of rules are used, there is an exponential growth in the number of parameters of the fuzzy system as the number of input dimensions increases. This “curse of dimensionality” effect leads to problems with design of fuzzy controllers (e.g., how to tune all these parameters), training of fuzzy estimators (e.g., complexity of a gradient algorithm for training, and problems with “over parameterization” that lead to poor convergence properties), and with computational complexity in the implementation for practical problems. We introduce a fuzzy system whose number of parameters grows linearly depending upon the number of inputs, even though it is constructed by using all possible combinations of the membership functions in defining the rules. We prove that this fuzzy system is equivalent to the standard fuzzy system as long as its parameters are specified in a certain way. Then, we show that it still holds the universal approximator property by using the Stone-Welerstrass theorem. Finally, we illustrate the performance of the fuzzy system via an application View full abstract»

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  • A fuzzy approach to 2D-shape recognition

    Publication Year: 2001 , Page(s): 5 - 16
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (224 KB)  

    This paper describes a method for fuzzy classification and recognition of 2D shapes, such as handwritten characters, image contours, etc. A fuzzy model is derived for each considered shape from a fuzzy description of a set of instances of this shape. A fuzzy description of a shape instance, in its turn, exploits appropriate fuzzy partitions of the two dimensions of the shape. These fuzzy partitions allow us to identify, and automatically associate an importance degree with the relevant shape zones for classification and recognition purposes. Two significant applications of the method are described, namely, recognition of olfactory signals and recognition of isolated, handwritten characters. In the former case, results are shown concerning the recognition of three different types of waste waters, collected in three different dilutions. In the latter case, results are shown concerning the application of the method to a NIST database, containing the segmented handprinted characters of 500 writers View full abstract»

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  • Fuzzy models to predict consumer ratings for biscuits based on digital image features

    Publication Year: 2001 , Page(s): 62 - 67
    Cited by:  Papers (12)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (116 KB)  

    Fuzzy models to recognize consumer preferences were developed as part of an automated inspection system for biscuits. Digital images were used to estimate the physical features of chocolate chip cookies including size, shape, baked dough color, and fraction of top surface area that was chocolate chips. Polls were conducted to determine consumer ratings of cookies. Four fuzzy models were developed to predict consumer ratings based on three of the features. There was substantial variation in consumer ratings in terms of individual opinions, as well as poll-to-poll differences. Parameters for the inference system, including fuzzy values for cookie features and consumer ratings, were defined based on the judgment and statistical analysis of data from the calibration polls. The two fuzzy models that gave satisfactory estimates of average consumer ratings are: the Mamdani inference system based on eight fuzzy values for consumer ratings; and the Sugeno inference system developed using the adaptive neurofuzzy inference system algorithm View full abstract»

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  • Fuzzy wavelet networks for function learning

    Publication Year: 2001 , Page(s): 200 - 211
    Cited by:  Papers (89)
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    Inspired by the theory of multiresolution analysis (MRA) of wavelet transforms and fuzzy concepts, a fuzzy wavelet network (FWN) is proposed for approximating arbitrary nonlinear functions. The FWN consists of a set of fuzzy rules. Each rule corresponding to a sub-wavelet neural network (WNN) consists of single-scaling wavelets. Through efficient bases selection, the dimension of the approximated function does not cause the bottleneck for constructing FWN. Especially, by learning the translation parameters of the wavelets and adjusting the shape of membership functions, the model accuracy and the generalization capability of the FWN can be remarkably improved. Furthermore, an algorithm for constructing and training the fuzzy wavelet networks is proposed. Simulation examples are also given to illustrate the effectiveness of the method View full abstract»

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  • A comparison of the performance of statistical and fuzzy algorithms for unexploded ordnance detection

    Publication Year: 2001 , Page(s): 17 - 30
    Cited by:  Papers (20)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (224 KB)  

    We focus on the development of signal processing algorithms that incorporate the underlying physics characteristic of the sensor and of the anticipated unexploded ordnance (UXO) target, in order to address the false alarm issue. In this paper, we describe several algorithms for discriminating targets from clutter that have been applied to data obtained with the multisensor towed array detection system (MTADS). This sensor suite includes both electromagnetic induction (EMI) and magnetometer sensors. We describe four signal processing techniques: a generalized likelihood ratio technique, a maximum likelihood estimation-based clustering algorithm, a probabilistic neural network, and a subtractive fuzzy clustering technique. These algorithms have been applied to the data measured by MTADS in a magnetically clean test pit and at a field demonstration. The results indicate that the application of advanced signal processing algorithms could provide up to a factor of two reduction in false alarm probability for the UXO detection problem View full abstract»

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  • An axiomatic approach of the discrete Sugeno integral as a tool to aggregate interacting criteria in a qualitative framework

    Publication Year: 2001 , Page(s): 164 - 172
    Cited by:  Papers (18)
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    We present a model allowing us to aggregate decision criteria when the available information is of a qualitative nature. The use of the Sugeno integral as an aggregation function is justified by an axiomatic approach. It is also shown that the mutual preferential independence of criteria reduces the Sugeno integral to a dictatorial aggregation View full abstract»

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  • Hybrid control for speed sensorless induction motor drive

    Publication Year: 2001 , Page(s): 116 - 138
    Cited by:  Papers (9)
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    The dynamic response of a hybrid-controlled speed sensorless induction motor (IM) drive is introduced. First, an adaptive observation system, which comprises speed and flux observers, is derived on the basis of model reference adaptive system (MRAS) theory. The speed observation system is implemented using a digital signal processor (DSP) with a high sampling rate to make it possible to achieve good dynamics. Next, based on the principle of computed torque control, a computed torque controller using the estimated speed signal is developed. Moreover, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a recurrent fuzzy neural network (RFNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Furthermore, based on Lyapunov stability a hybrid control system, which combines the computed torque controller, the RFNN uncertainty observer and a compensated controller, is proposed to control the rotor speed of the sensorless IM drive. The computed torque controller with RFNN uncertainty observer is the main tracking controller and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rules of the RFNN. Finally, the effectiveness of the proposed observation and control systems is verified by simulated and experimental results View full abstract»

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  • New trends in recognizing experimental drives: fuzzy logic and formal language theories

    Publication Year: 2001 , Page(s): 68 - 87
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (408 KB)  

    Drive systems today determine the productivity and quality of industrial processes. However, they exhibit considerable complexities related with their behavior as large uncertainties at a structure and parameter levels, multidimensionality, and strong mutual interactions. This paper aims to analyze common features, and the potential, but also the drawbacks that fuzzy logic and formal language theories show when used for recognition of patterns in experimental drives. Two prototype systems are used: an electrohydraulic drive and an induction motor drive. We underline the similarities and various aspects of the recognition methodologies, despite their use on different systems. A set of experimental learning situations with critical effects on their performance are presented and discussed View full abstract»

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  • MPEG VBR video traffic modeling and classification using fuzzy technique

    Publication Year: 2001 , Page(s): 183 - 193
    Cited by:  Papers (58)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (304 KB)  

    We present an approach for MPEG variable bit rate (VBR) video modeling and classification using fuzzy techniques. We demonstrate that a type-2 fuzzy membership function, i.e., a Gaussian MF with uncertain variance, is most appropriate to model the log-value of I/P/B frame sizes in MPEG VBR video. The fuzzy c-means (FCM) method is used to obtain the mean and standard deviation (std) of T/P/B frame sizes when the frame category is unknown. We propose to use type-2 fuzzy logic classifiers (FLCs) to classify video traffic using compressed data. Five fuzzy classifiers and a Bayesian classifier are designed for video traffic classification, and the fuzzy classifiers are compared against the Bayesian classifier. Simulation results show that a type-2 fuzzy classifier in which the input is modeled as a type-2 fuzzy set and antecedent membership functions are modeled as type-2 fuzzy sets performs the best of the five classifiers when the testing video product is not included in the training products and a steepest descent algorithm is used to tune its parameters View full abstract»

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  • Fuzzy modeling of client preference from large data sets: an application to target selection in direct marketing

    Publication Year: 2001 , Page(s): 153 - 163
    Cited by:  Papers (16)  |  Patents (2)
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    Advances in computational methods have led, in the world of financial services, to huge databases of client and market information. In the past decade, various computational intelligence techniques have been applied in mining this data for obtaining knowledge and in-depth information about the clients and the markets. The paper discusses the application of fuzzy clustering in target selection from large databases for direct marketing purposes. Actual data from the campaigns of a large financial services provider are used as a test case. The results obtained with the fuzzy clustering approach are compared with those resulting from the current practice of using statistical tools for target selection View full abstract»

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  • Graph matching by relaxation of fuzzy assignments

    Publication Year: 2001 , Page(s): 173 - 182
    Cited by:  Papers (10)  |  Patents (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (268 KB)  

    Graphs are very powerful and widely used representational tools in computer applications. We present a relaxation approach to (sub)graph matching based on a fuzzy assignment matrix. The algorithm has a computational complexity of O(n2m2) where n and m are the number of nodes in the two graphs being matched, and can perform both exact and inexact matching. To illustrate the performance of the algorithm, we summarize the results obtained for more than 12 000 pairs of graphs of varying types (weighted graphs, attributed graphs, and noisy graphs). We also compare our results with those obtained using the graduated assignment algorithm View full abstract»

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  • Noisy speech processing by recurrently adaptive fuzzy filters

    Publication Year: 2001 , Page(s): 139 - 152
    Cited by:  Papers (18)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (292 KB)  

    Two noisy speech processing problems-speech enhancement and noisy speech recognition-are dealt with. The technique we focus on is by using the filtering approach; a novel filter, the recurrently adaptive fuzzy filter (RAFF), is proposed and applied to these two problems. The speech enhancement is based on adaptive noise cancellation with two microphones, where the RAFF is used to eliminate the noise corrupting the desired speech signal in the primary channel. As to the noisy speech recognition, the RAFF is used to filter the noise in the feature domain of speech signals. The RAFF is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. As compared to other existing nonlinear filters, three major advantages of the RAFF are observed: 1) a priori knowledge can be incorporated into the RAFF, which makes the fusion of numerical data and linguistic information possible; 2) owing to the dynamic property of the RAFF, the exact lagged order of the input variables need not be known in advance; 3) no predetermination, like the number of hidden nodes, must be given since the RAFF can find its optimal structure and parameters automatically Several examples on adaptive noise cancellation and noisy speech recognition problems using the RAFF are illustrated to demonstrate the performance of the RAFF View full abstract»

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  • Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks

    Publication Year: 2001 , Page(s): 88 - 101
    Cited by:  Papers (10)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (320 KB)  

    An input variable selection procedure is introduced for the identification and construction of multi-input multi-output (MIMO) neurofuzzy operating point dependent models. The algorithm is an extension of a forward modified Gram-Schmidt orthogonal least squares procedure for a linear model structure which is modified to accommodate nonlinear system modeling by incorporating piecewise locally linear model fitting. The proposed input nodes selection procedure effectively tackles the problem of the curse of dimensionality associated with lattice-based modeling algorithms such as radial basis function neurofuzzy networks, enabling the resulting neurofuzzy operating point dependent model to be widely applied in control and estimation. Some numerical examples are given to demonstrate the effectiveness of the proposed construction algorithm View full abstract»

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  • Automatic vehicle detection in infrared imagery using a fuzzy inference-based classification system

    Publication Year: 2001 , Page(s): 53 - 61
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (128 KB)  

    This paper describes a unique approach of using a fuzzy inference system for target detection and classification. It first describes the methods that are used to identify regions of interest within each frame of the infrared imagery. Next, the specific data features that are extracted from these regions of interest are described. The fuzzy inference system used in this application is described. This description includes discussions of the feature input and system output membership functions, the rules used in the inference system, and the logical operations, implication, aggregation and defuzzification methods employed. Finally, results attained by applying the described approach to a “blind” closing sequence data set are provided and conclusions are drawn. The developed techniques have proved to be robust and have demonstrated an ability to properly classify a variety of targets in different clutter environments. The described approach can easily be expanded to utilize other feature inputs View full abstract»

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  • A new approach to target recognition for LADAR data

    Publication Year: 2001 , Page(s): 44 - 52
    Cited by:  Papers (7)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (224 KB)  

    We discuss target detection in LADAR intensity images. Thirteen features, eleven of which come from an asymmetric co-occurrence matrix, are extracted from region-of-interest windows in each image. Two methods of feature selection are applied to the extracted vectors. Random selection leads to a pair of selected features for a nearest-neighbor rule (1-nn) detector. Extended backpropagation leads to six selected features using a modified multilayered perceptron (MLP) network. The 1-nn detector achieves a test-error rate of about 16% at a false-alarm rate of 8%. The MLP has a test-error rate of about 12% with a false-alarm rate of 6% View full abstract»

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  • Recognition technology for the detection of buried land mines

    Publication Year: 2001 , Page(s): 31 - 43
    Cited by:  Papers (22)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (236 KB)  

    As described by Zadeh, recognition technology refers to systems that incorporate new sensors, novel signal processing, and soft computing. In this paper, we discuss these principles applied to the problem of land mine detection. We describe a complex recognition system that is evolving from basic research into a fielded system. Some components of this system have been field tested with excellent results, whereas other components have achieved such results in the laboratory. Fuzzy set-based information fusion algorithms are central to the excellent results obtained. Multiple-detection algorithms are applied to signals acquired from an innovative ground penetrating radar that produces volumetric sub-surface imagery. The outputs of the detection algorithms are combined using the fuzzy logic and Sugeno and Choquet fuzzy integrals to produce overall detection scores. Experimental results are provided on training data and on completely blind test data collected in the field and scored by the US Army 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