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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Issue 5 • Date Oct. 2002

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Displaying Results 1 - 16 of 16
  • Call for papers

    Publication Year: 2002 , Page(s): 700
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    Freely Available from IEEE
  • Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems

    Publication Year: 2002 , Page(s): 583 - 597
    Cited by:  Papers (56)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (603 KB) |  | HTML iconHTML  

    A new hybrid direct/indirect adaptive fuzzy neural network (FNN) controller with a state observer and supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by an observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which can be adjusted by the tradeoff between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive FNN controller and direct adaptive FNN controller. Furthermore, a supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be deactivated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Two nonlinear systems, namely, inverted pendulum system and Chua's (1989) chaotic circuit, are fully illustrated to track sinusoidal signals. The resulting hybrid direct/indirect FNN control systems show better performances, i.e., tracking error and control effort can be made smaller and it is more flexible during the design process. View full abstract»

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  • Web newspaper layout optimization using simulated annealing

    Publication Year: 2002 , Page(s): 686 - 691
    Cited by:  Papers (13)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (753 KB)  

    The Web newspaper pagination problem consists of optimizing the layout of a set of articles extracted from several Web newspapers and sending it to the user as the result of a previous query. This layout should be organized in columns, as in real newspapers, and should be adapted to the client Web browser configuration in real time. This paper presents an approach to the problem based on simulated annealing (SA) that solves the problem on-line, adapts itself to the client's computer configuration, and supports articles with different widths. View full abstract»

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  • Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models

    Publication Year: 2002 , Page(s): 612 - 621
    Cited by:  Papers (87)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (380 KB)  

    The construction of interpretable Takagi-Sugeno (TS) fuzzy models by means of clustering is addressed. First, it is shown how the antecedent fuzzy sets and the corresponding consequent parameters of the TS model can be derived from clusters obtained by the Gath-Geva (GG) algorithm. To preserve the partitioning of the antecedent space, linearly transformed input variables can be used in the model. This may, however, complicate the interpretation of the rules. To form an easily interpretable model that does not use the transformed input variables, a new clustering algorithm is proposed, based on the expectation-maximization (EM) identification of Gaussian mixture models. This new technique is applied to two well-known benchmark problems: the MPG (miles per gallon) prediction and a simulated second-order nonlinear process. The obtained results are compared with results from the literature. View full abstract»

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  • Hierarchical semi-numeric method for pairwise fuzzy group decision making

    Publication Year: 2002 , Page(s): 691 - 700
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (449 KB) |  | HTML iconHTML  

    Gradual improvements to a single-level semi-numeric method, i.e., linguistic labels preference representation by fuzzy sets computation for pairwise fuzzy group decision making are summarized. The method is extended to solve multiple criteria hierarchical structure pairwise fuzzy group decision-making problems. The problems are hierarchically structured into focus, criteria, and alternatives. Decision makers express their evaluations of criteria and alternatives based on each criterion by using linguistic labels. The labels are converted into and processed in triangular fuzzy numbers (TFNs). Evaluations of criteria yield relative criteria weights. Evaluations of the alternatives, based on each criterion, yield a degree of preference for each alternative or a degree of satisfaction for each preference value. By using a neat ordered weighted average (OWA) or a fuzzy weighted average operator, solutions obtained based on each criterion are aggregated into final solutions. The hierarchical semi-numeric method is suitable for solving a larger and more complex pairwise fuzzy group decision-making problem. The proposed method has been verified and applied to solve some real cases and is compared to Saaty's (1996) analytic hierarchy process (AHP) method. View full abstract»

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  • An efficient algorithm for automatically generating multivariable fuzzy systems by Fourier series method

    Publication Year: 2002 , Page(s): 622 - 629
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (364 KB) |  | HTML iconHTML  

    By exploiting the Fourier series expansion, we have developed a new constructive method of automatically generating a multivariable fuzzy inference system from any given sample set with the resulting multivariable function being constructed within any specified precision to the original sample set. The given sample sets are first decomposed into a cluster of simpler sample sets such that a single input fuzzy system is constructed readily for a sample set extracted directly from the cluster independent of the other variables. Once the relevant fuzzy rules and membership functions are constructed for each of the variables completely independent of the other variables, the resulting decomposed fuzzy rules and membership functions are integrated back into the fuzzy system appropriate for the original sample set requiring only a moderate cost of computation in the required decomposition and composition processes. After proving two basic theorems which we need to ensure the validity of the decomposition and composition processes of the system construction, we have demonstrated a constructive algorithm of a multivariable fuzzy system. Exploiting an implicit error bound analysis available at each of the construction steps, the present Fourier method is capable of implementing a more stable fuzzy system than the power series expansion method of ParNeuFuz and PolyNeuFuz, covering and implementing a wider range of more robust applications. View full abstract»

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  • A generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms

    Publication Year: 2002 , Page(s): 571 - 582
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (314 KB)  

    Segmentation of an image into regions and the labeling of the regions is a challenging problem. In this paper, an approach that is applicable to any set of multifeature images of the same location is derived. Our approach applies to, for example, medical images of a region of the body; repeated camera images of the same area; and satellite images of a region. The segmentation and labeling approach described here uses a set of training images and domain knowledge to produce an image segmentation system that can be used without change on images of the same region collected over time. How to obtain training images, integrate domain knowledge, and utilize learning to segment and label images of the same region taken under any condition for which a training image exists is detailed. It is shown that clustering in conjunction with image processing techniques utilizing an iterative approach can effectively identify objects of interest in images. The segmentation and labeling approach described here is applied to color camera images and two other image domains are used to illustrate the applicability of the approach. View full abstract»

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  • Embedding fuzzy mechanisms and knowledge in box-type reinforcement learning controllers

    Publication Year: 2002 , Page(s): 645 - 653
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (297 KB) |  | HTML iconHTML  

    In this paper, we report our study on embedding fuzzy mechanisms and knowledge into box-type reinforcement learning controllers. One previous approach for incorporating fuzzy mechanisms can only achieve one successful run out of nine tests compared to eight successful runs in a nonfuzzy learning control scheme. After analysis, the credit assignment problem and the weighting domination problem are identified. Furthermore, the use of fuzzy mechanisms in temporal difference seems to play a negative factor. Modifications to overcome those problems are proposed. Furthermore, several remedies are employed in that approach. The effects of those remedies applied to our learning scheme are presented and possible variations are also studied. Finally, the issue of incorporating knowledge into reinforcement learning systems is studied. From our simulations, it is concluded that the use of knowledge for the control network can provide good learning results, but the use of knowledge for the evaluation network alone seems unable to provide any significant advantages. Furthermore, we also employ Makarovic's (1988) rules as the knowledge for the initial setting of the control network. In our study, the rules are separated into four groups to avoid the ordering problem. View full abstract»

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  • A reduction approach for fuzzy rule bases of fuzzy controllers

    Publication Year: 2002 , Page(s): 668 - 675
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (342 KB) |  | HTML iconHTML  

    In this paper, a new approach to reducing the number of rules in a given fuzzy rule base of a fuzzy controller is presented. The fuzzy mechanism of the fuzzy controller under consideration consists of the product-sum inference, singleton output consequents and centroid defuzzification. The output consequents in the cells of the rule table are collected and represented as an output consequent matrix. The feature of the output consequent matrix is extracted by the singular values of the matrix. The output consequent matrix is reasonably approximated with a dominant consequent matrix. Also, the elements of the dominant consequent matrix is determined to minimize the approximation error function. Then the size of the dominant consequent matrix (the size of the fuzzy rule base) is reduced through the rule combination approach. The scaling factors for the fuzzy controller with the reduced rule table are adjusted to have the control system satisfy the performance indices. The effectiveness of the proposed approach is shown using simulation and experimental results. View full abstract»

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  • Defect detection in textured materials using optimized filters

    Publication Year: 2002 , Page(s): 553 - 570
    Cited by:  Papers (22)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1519 KB) |  | HTML iconHTML  

    The problem of automated defect detection in textured materials is investigated. A new approach for defect detection using linear FIR filters with optimized energy separation is proposed. The performance of different feature separation criteria with reference to fabric defects has been evaluated. The issues relating to the design of optimal filters for supervised and unsupervised web inspection are addressed. A general web inspection system based on the optimal filters is proposed. The experiments on this new approach have yielded excellent results. The low computational requirement confirms the usefulness of the approach for industrial inspection. View full abstract»

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  • Complexity reduction for "large image" processing

    Publication Year: 2002 , Page(s): 598 - 611
    Cited by:  Papers (32)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (677 KB) |  | HTML iconHTML  

    We present a method for sampling feature vectors in large (e.g., 2000 × 5000 × 16 bit) images that finds subsets of pixel locations which represent c "regions" in the image. Samples are accepted by the chi-square (χ2) or divergence hypothesis test. A framework that captures the idea of efficient extension of image processing algorithms from the samples to the rest of the population is given. Computationally expensive (in time and/or space) image operators (e.g., neural networks (NNs) or clustering models) are trained on the sample, and then extended noniteratively to the rest of the population. We illustrate the general method using fuzzy c-means (FCM) clustering to segment Indian satellite images. On average, the new method can achieve about 99% accuracy (relative to running the literal algorithm) using roughly 24% of the image for training. This amounts to an average savings of 76% in CPU time. We also compare our method to its closest relative in the group of schemes used to accelerate FCM: our method averages a speedup of about 4.2, whereas the multistage random sampling approach achieves an average acceleration of 1.63. View full abstract»

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  • Generating learning sequences for decision makers through data mining and competence set expansion

    Publication Year: 2002 , Page(s): 679 - 686
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (407 KB) |  | HTML iconHTML  

    For each decision problem, there is a competence set, proposed by Yu (1990), consisting of ideas, knowledge, information, and skills required for solving the problem. Thus, it is reasonable that we view a set of useful patterns discovered from a relational database by data mining techniques as a needed competence set for solving one problem. Significantly, when decision makers have not acquired the competence set, they may lack confidence in making decisions. In order to effectively acquire a needed competence set to cope with the corresponding problem, it is necessary to find appropriate learning sequences for acquiring those useful patterns, the so-called competence set expansion. This paper thus proposes an effective method consisting of two phases to generate learning sequences. The first phase finds a competence set consisting of useful patterns by using a proposed data mining technique. The other phase expands that competence set with minimum learning cost by the minimum spanning table method (Feng and Yu (1998)). From a numerical example, we can see that it is possible to help decision makers to solve the decision problems by use of the data mining technique and the competence set expansion, enabling them to make better decisions. View full abstract»

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  • A dual neural network for constrained joint torque optimization of kinematically redundant manipulators

    Publication Year: 2002 , Page(s): 654 - 662
    Cited by:  Papers (35)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (475 KB) |  | HTML iconHTML  

    A dual neural network is presented for the real-time joint torque optimization of kinematically redundant manipulators, which corresponds to global kinetic energy minimization of robot mechanisms. Compared to other computational strategies on inverse kinematics, the dual network is developed at the acceleration level to resolve redundancy of limited-joint-range manipulators. The dual network has a simple architecture with only one layer of neurons and is proved to be globally exponentially convergent to optimal solutions. The dual neural network is simulated with the PUMA 560 robot arm to demonstrate effectiveness. View full abstract»

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  • Learning nonlinear multiregression networks based on evolutionary computation

    Publication Year: 2002 , Page(s): 630 - 644
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (470 KB)  

    This paper describes a novel knowledge discovery and data mining framework dealing with nonlinear interactions among domain attributes. Our network-based model provides an effective and efficient reasoning procedure to perform prediction and decision making. Unlike many existing paradigms based on linear models, the attribute relationship in our framework is represented by nonlinear nonnegative multiregressions based on the Choquet integral. This kind of multiregression is able to model a rich set of nonlinear interactions directly. Our framework involves two layers. The outer layer is a network structure consisting of network elements as its components, while the inner layer is concerned with a particular network element modeled by Choquet integrals. We develop a fast double optimization algorithm (FDOA) for learning the multiregression coefficients of a single network element. Using this local learning component and multiregression-residual-cost evolutionary programming (MRCEP), we propose a global learning algorithm, called MRCEP-FDOA, for discovering the network structures and their elements from databases. We have conducted a series of experiments to assess the effectiveness of our algorithm and investigate the performance under different parameter combinations, as well as sizes of the training data sets. The empirical results demonstrate that our framework can successfully discover the target network structure and the regression coefficients. View full abstract»

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  • An evolutionary tabu search for cell image segmentation

    Publication Year: 2002 , Page(s): 675 - 678
    Cited by:  Papers (7)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (271 KB) |  | HTML iconHTML  

    Many engineering problems can be formulated as optimization problems. It has become more and more important to develop an efficient global optimization technique for solving these problems. In this paper, we propose an evolutionary tabu search (ETS) for cell image segmentation. The advantages of genetic algorithms (GA) and TS algorithms are incorporated into the proposed method. More precisely, we incorporate "the survival of the fittest" from evolutionary algorithms into TS. The method has been applied to the segmentation of several kinds of cell images. The experimental results show that the new algorithm is a practical and effective one for global optimization; it can yield good, near-optimal solutions and has better convergence and robustness than other global optimization approaches. View full abstract»

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  • A merge-based condensing strategy for multiple prototype classifiers

    Publication Year: 2002 , Page(s): 662 - 668
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (273 KB) |  | HTML iconHTML  

    A class-conditional hierarchical clustering framework has been used to generalize and improve previously proposed condensing schemes to obtain multiple prototype classifiers. The proposed method conveniently uses geometric properties and clusters to efficiently obtain reduced sets of prototypes that accurately represent the data while significantly keeping its discriminating power. The benefits of the proposed approach are empirically assessed with regard to other previously proposed algorithms which are similar in their foundations. Other well-known multiple prototype classifiers have also been taken into account in the comparison. View full abstract»

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Aims & Scope

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics focuses on cybernetics, including communication and control across humans, machines and organizations at the structural or neural level

 

This Transaction ceased production in 2012. The current retitled publication is IEEE Transactions on Cybernetics.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Dr. Eugene Santos, Jr.
Thayer School of Engineering
Dartmouth College