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

Issue 2 • Date Apr 2003

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Displaying Results 1 - 16 of 16
  • Toward a language for specifying summarizing statistics

    Page(s): 177 - 187
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (714 KB) |  | HTML iconHTML  

    We introduce the ordered weighted averaging (OWA) operator and discuss how it can provide a basis for generating summarizing statistics over large data sets. We further note how different forms of OWA operators can be induced using weight generating functions. We show how these weight generating functions can provide a vehicle with which a data analyst can express desired summarizing statistics. Our goal is to develop an understanding of the relationship between weight generating functions and resulting summarizing statistics. View full abstract»

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  • Credit assigned CMAC and its application to online learning robust controllers

    Page(s): 202 - 213
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (655 KB) |  | HTML iconHTML  

    In this paper, a novel learning scheme is proposed to speed up the learning process in cerebellar model articulation controllers (CMAC). In the conventional CMAC learning scheme, the correct numbers of errors are equally distributed into all addressed hypercubes, regardless of the credibility of the hypercubes. The proposed learning approach uses the inverse of learned times of the addressed hypercubes as the credibility (confidence) of the learned values, resulting in learning speed becoming very fast. To further demonstrate online learning capability of the proposed credit assigned CMAC learning scheme, this paper also presents a learning robust controller that can actually learn online. Based on robust controllers presented in the literature, the proposed online learning robust controller uses previous control input, current output acceleration, and current desired output as the state to define the nominal effective moment of the system from the CMAC table. An initial trial mechanism for the early learning stage is also proposed. With our proposed credit-assigned CMAC, the robust learning controller can accurately trace various trajectories online. View full abstract»

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  • Genetic design of biologically inspired receptive fields for neural pattern recognition

    Page(s): 258 - 270
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2173 KB) |  | HTML iconHTML  

    This paper proposes a new method for the design, through simulated evolution, of biologically inspired receptive fields in feedforward neural networks (NNs). The method is intended to enhance pattern recognition performance by creating new neural architectures specifically tuned for a particular pattern recognition problem. It proposes a combined neural architecture composed of two networks in cascade: a feature extraction network (FEN) followed by a neural classifier. The FEN is composed of several layers with receptive fields constructed by additive superposition of excitatory and inhibitory fields. A genetic algorithm (GA) is used to select receptive field parameters to improve classification performance. The parameters are receptive field size, orientation, and bias as well as the number of different receptive fields in each layer. Based on a random initial population where each individual represents a different neural architecture, the GA creates new enhanced individuals. The method is applied to handwritten digit classification and face recognition. In both problems, results show strong dependency between NN classification performance and receptive field architecture. GA selected parameters of the receptive fields produced improvements in the classification performance on the test set up to 90.8% for the problem of handwritten digit classification and up to 84.2% for the face recognition problem. On the same test sets, results were compared advantageously to standard feedforward multilayer perceptron (MLP) NNs where receptive fields are not explicitly defined. The MLP reached a maximum classification performance of 84.9% and 77.5% in both problems, respectively. View full abstract»

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  • Analytical development of dynamic equations of motion for a three-dimensional flexible link manipulator with revolute and prismatic joints

    Page(s): 237 - 249
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1061 KB) |  | HTML iconHTML  

    In this paper, a mathematical model capable of handling a three-dimensional (3D) flexible n-degree of freedom manipulator having both revolute and prismatic joints is considered. This model is used to study the longitudinal, transversal, and torsional vibration characteristics of the robot manipulator and obtain kinematic and dynamic equations of motion. The presence of prismatic joints makes the mathematical derivation complex. In this paper, for the first time, prismatic joints as well as revolute joints have been considered in the structure of a 3D flexible n-degree of freedom manipulator. The kinematic and dynamic equations of motion representing longitudinal, transversal, and torsional vibration characteristics have been solved in parametric form with no discretization. In this investigation, in order to obtain an analytical solution of the vibrational equations, a novel approach is presented using the perturbation method. By solving the equations of motion, it is shown that mode shapes of the link with prismatic joints can be modeled as the equivalent clamped beam at each time instant. As an example, this method is applied to a three degrees of freedom robot with revolute and prismatic joints. The obtained equations are solved using the perturbation method and the results are used to simulate vibrational behavior of the manipulator. View full abstract»

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  • Evolutionary learning of hierarchical decision rules

    Page(s): 324 - 331
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (398 KB) |  | HTML iconHTML  

    This paper describes an approach based on evolutionary algorithms, hierarchical decision rules (HIDER), for learning rules in continuous and discrete domains. The algorithm produces a hierarchical set of rules, that is, the rules are sequentially obtained and must therefore be tried until one is found whose conditions are satisfied. Thus, the number of rules may be reduced because the rules could be inside of one another. The evolutionary algorithm uses both real and binary coding for the individuals of the population. We tested our system on real data from the UCI repository, and the results of a ten-fold cross-validation are compared to C4.5s, C4.5Rules, See5s, and See5Rules. The experiments show that HIDER works well in practice. View full abstract»

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  • TASOM: a new time adaptive self-organizing map

    Page(s): 271 - 282
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (743 KB) |  | HTML iconHTML  

    The time adaptive self-organizing map (TASOM) network is a modified self-organizing map (SOM) network with adaptive learning rates and neighborhood sizes as its learning parameters. Every neuron in the TASOM has its own learning rate and neighborhood size. For each new input vector, the neighborhood size and learning rate of the winning neuron and the learning rates of its neighboring neurons are updated. A scaling vector is also employed in the TASOM algorithm for compensation against scaling transformations. Analysis of the updating rules of the algorithm reveals that the learning parameters may increase or decrease for adaptation to a changing environment, such that the minimum increase or decrease is achieved according to a specific measure. Several versions of the TASOM-based networks are proposed in this paper for different applications, including bilevel thresholding of grey level images, tracking of moving objects and their boundaries, and adaptive clustering. Simulation results show satisfactory performance of the proposed methods in the implemented applications. View full abstract»

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  • Aspect graph construction with noisy feature detectors

    Page(s): 340 - 351
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1091 KB)  

    Many three-dimensional (3D) object recognition strategies use aspect graphs to represent objects in the model base. A crucial factor in the success of these object recognition strategies is the accurate construction of the aspect graph, its ease of creation, and the extent to which it can represent all views of the object for a given setup. Factors such as noise and nonadaptive thresholds may introduce errors in the feature detection process. This paper presents a characterization of errors in aspect graphs, as well as an algorithm for estimating aspect graphs, given noisy sensor data. We present extensive results of our strategies applied on a reasonably complex experimental set, and demonstrate applications to a robust 3D object recognition problem. View full abstract»

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  • Agents that react to changing market situations

    Page(s): 188 - 201
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1409 KB) |  | HTML iconHTML  

    Market-driven agents are negotiation agents that react to changing market situations by making adjustable rates of concession. This paper presents 1) the foundations for designing market-driven strategies of agents, 2) a testbed of market-driven agents, 3) experimental results in simulating the market-driven approach, and 4) theoretical analyses of agents' performance in extremely large markets. In determining the amount of concession for each trading cycle, market-driven agents in this research are guided by four mathematical functions of eagerness, remaining trading time, trading opportunity , and competition. At different stages of trading, agents may adopt different trading strategies, and make different rates of concession. Four classes of strategies with respect to remaining trading time are discussed. Trading opportunity is determined by considering: 1) number of trading partners, 2) spreads-differences in utilities between an agent and its trading partners, and 3) probability of completing a deal. While eagerness represents an agent's desire to trade, trading competition is determined by the probability that it is not considered as the most preferred trader by its trading partners. Experimental results and theoretical analyses showed that agents guided by market-driven strategies 1) react to changing market situations by making prudent and appropriate rates of concession, and 2) achieve trading outcomes that correspond to intuitions in real-life trading. View full abstract»

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  • Design and stability analysis of fuzzy model-based nonlinear controller for nonlinear systems using genetic algorithm

    Page(s): 250 - 257
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (492 KB)  

    This paper presents stability analysis of fuzzy model-based nonlinear control systems, and the design of nonlinear gains and feedback gains of the nonlinear controller using a genetic algorithm (GA) with arithmetic crossover and nonuniform mutation. A stability condition is derived based on Lyapunov's stability theory with a smaller number of Lyapunov conditions. A solution of the stability conditions is also determined using GA. An application example of stabilizing a cart-pole typed inverted pendulum system is given to show the stabilizability of the nonlinear controller. View full abstract»

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  • Identification of probabilistic cellular automata

    Page(s): 225 - 236
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (882 KB)  

    The identification of probabilistic cellular automata (PCA) is studied using a new two stage neighborhood detection algorithm. It is shown that a binary probabilistic cellular automaton (BPCA) can be described by an integer-parameterized polynomial corrupted by noise. Searching for the correct neighborhood of a BPCA is then equivalent to selecting the correct terms which constitute the polynomial model of the BPCA, from a large initial term set. It is proved that the contribution values for the correct terms can be calculated independently of the contribution values for the noise terms. This allows the neighborhood detection technique developed for deterministic rules in to be applied with a larger cutoff value to discard the majority of spurious terms and to produce an initial presearch for the BPCA neighborhood. A multiobjective genetic algorithm (GA) search with integer constraints is then evolved to refine the reduced neighborhood and to identify the polynomial rule which is equivalent to the probabilistic rule with the largest probability. A probability table representing the BPCA can then be determined based on the identified neighborhood and the deterministic rule. The new algorithm is tested over a large set of one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) BPCA rules. Simulation results demonstrate the efficiency of the new method. View full abstract»

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  • Identification of the neighborhood and CA rules from spatio-temporal CA patterns

    Page(s): 332 - 339
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (434 KB) |  | HTML iconHTML  

    Extracting the rules from spatio-temporal patterns generated by the evolution of cellular automata (CA) usually produces a CA rule table without providing a clear understanding of the structure of the neighborhood or the CA rule. In this paper, a new identification method based on using a modified orthogonal least squares or CA-OLS algorithm to detect the neighborhood structure and the underlying polynomial form of the CA rules is proposed. The Quine-McCluskey method is then applied to extract minimum Boolean expressions from the polynomials. Spatio-temporal patterns produced by the evolution of 1D, 2D, and higher dimensional binary CAs are used to illustrate the new algorithm, and simulation results show that the CA-OLS algorithm can quickly select both the correct neighborhood structure and the corresponding rule. View full abstract»

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  • Adaptive fuzzy sliding mode controller for linear systems with mismatched time-varying uncertainties

    Page(s): 283 - 294
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (687 KB) |  | HTML iconHTML  

    A new design approach for an adaptive fuzzy sliding mode controller (AFSMC) for linear systems with mismatched time-varying uncertainties is presented. The coefficient matrix of the sliding function can be designed to satisfy a sliding coefficient matching condition provided time-varying uncertainties are bounded. With the sliding coefficient matching condition satisfied, an AFSMC is proposed to stabilize the uncertain system. The parameters of output fuzzy sets in the fuzzy mechanism are on-line adapted to improve the performance of the fuzzy sliding mode control system. The bounds of uncertainties are not required to be known in advance for the AFSMC. Stability of the fuzzy control system is guaranteed and the system is shown to be invariant on the sliding surface. Moreover, chattering around the sliding surface in sliding mode control can be reduced by the proposed design approach. Simulation results are included to illustrate the effectiveness of the proposed AFSMC. View full abstract»

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  • Stereo geometry from 3D ego-motion streams

    Page(s): 308 - 323
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1165 KB)  

    This paper addresses the problem of geometry determination of a stereo rig that undergoes general rigid motions. Neither known reference objects nor stereo correspondence are required. With almost no exception, all existing online solutions attempt to recover stereo geometry by first establishing stereo correspondences. We first describe a mathematical framework that allows us to solve for stereo geometry, i.e., the rotation and translation between the two cameras, using only motion correspondence that is far easier to acquire than stereo correspondence. Second, we show how to recover the rotation and present two linear methods, as well as a nonlinear one to solve for the translation. Third, we perform a stability study for the developed methods in the presence of image noise, camera parameter noise, and ego-motion noise. We also address accuracy issues. Experiments with real image data are presented. The work allows the concept of online calibration to be broadened, as it is no longer true that only single cameras can exploit structure-from-motion strategies; even the extrinsic parameters of a stereo rig of cameras can do so without solving stereo correspondence. The developed framework is applicable for estimating the relative three-dimensional (3D) geometry associated with a wide variety of mounted devices used in vision and robotics, by exploiting their scaled ego-motion streams. View full abstract»

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  • Equilibrium relations and bipolar cognitive mapping for online analytical processing with applications in international relations and strategic decision support

    Page(s): 295 - 307
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (998 KB) |  | HTML iconHTML  

    Bipolar logic, bipolar sets, and equilibrium relations are proposed for bipolar cognitive mapping and visualization in online analytical processing (OLAP) and online analytical mining (OLAM). As cognitive models, cognitive maps (CMs) hold great potential for clustering and visualization. Due to the lack of a formal mathematical basis, however, CM-based OLAP and OLAM have not gained popularity. Compared with existing approaches, bipolar cognitive mapping has a number of advantages. First, bipolar CMs are formal logical models as well as cognitive models. Second, equilibrium relations (with polarized reflexivity, symmetry, and transitivity), as bipolar generalizations and fusions of equivalence relations, provide a theoretical basis for bipolar visualization and coordination. Third, an equilibrium relation or CM induces bipolar partitions that distinguish disjoint coalition subsets not involved in any conflict, disjoint coalition subsets involved in a conflict, disjoint conflict subsets, and disjoint harmony subsets. Finally, equilibrium energy analysis leads to harmony and stability measures for strategic decision and multiagent coordination. Thus, this work bridges a gap for CM-based clustering and visualization in OLAP and OLAM. Basic ideas are illustrated with example CMs in international relations. View full abstract»

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  • Modeling uncertainty reasoning with possibilistic Petri nets

    Page(s): 214 - 224
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (623 KB) |  | HTML iconHTML  

    Manipulation of perceptions is a remarkable human capability in a wide variety of physical and mental tasks under fuzzy or uncertain surroundings. Possibilistic reasoning can be treated as a mechanism that mimics human inference mechanisms with uncertain information. Petri nets are a graphical and mathematical modeling tool with powerful modeling and analytical ability. The focus of this paper is on the integration of Petri nets with possibilistic reasoning to reap the benefits of both formalisms. This integration leads to a possibilistic Petri nets model (PPN) with the following features. A possibilistic token carries information to describe an object and its corresponding possibility and necessity measures. Possibilistic transitions are classified into four types: inference transitions, duplication transitions, aggregation transitions, and aggregation-duplication transitions. A reasoning algorithm, based on possibilistic Petri nets, is also presented to improve the efficiency of possibilistic reasoning and an example related to diagnosis of cracks in reinforced concrete structures is used to illustrate the proposed approach. View full abstract»

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  • Reinforcement learning for high-level fuzzy Petri nets

    Page(s): 351 - 362
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (531 KB) |  | HTML iconHTML  

    The author has developed a reinforcement learning algorithm for the high-level fuzzy Petri net (HLFPN) models in order to perform structure and parameter learning simultaneously. In addition to the HLFPN itself, the difference and similarity among a variety of subclasses concerning Petri nets are also discussed. As compared with the fuzzy adaptive learning control network (FALCON), the HLFPN model preserves the advantages that: 1) it offers more flexible learning capability because it is able to model both IF-THEN and IF-THEN-ELSE rules; 2) it allows multiple heterogeneous outputs to be drawn if they exist; 3) it offers a more compact data structure for fuzzy production rules so as to save information storage; and 4) it is able to learn faster due to its structural reduction. Finally, main results are presented in the form of seven propositions and supported by some experiments. 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