By Topic

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

Issue 1 • Date Feb 1996

Filter Results

Displaying Results 1 - 22 of 22
  • Ant system: optimization by a colony of cooperating agents

    Page(s): 29 - 41
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1360 KB)  

    An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An automatic indexing and neural network approach to concept retrieval and classification of multilingual (Chinese-English) documents

    Page(s): 75 - 88
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1508 KB)  

    An automatic indexing and concept classification approach to a multilingual (Chinese and English) bibliographic database is presented. We introduced a multi-linear term-phrasing technique to extract concept descriptors (terms or keywords) from a Chinese-English bibliographic database. A concept space of related descriptors was then generated using a co-occurrence analysis technique. Like a man-made thesaurus, the system-generated concept space can be used to generate additional semantically-relevant terms for search. For concept classification and clustering, a variant of a Hopfield neural network was developed to cluster similar concept descriptors and to generate a small number of concept groups to represent (summarize) the subject matter of the database. The concept space approach to information classification and retrieval has been adopted by the authors in other scientific databases and business applications, but multilingual information retrieval presents a unique challenge. This research reports our experiment on multilingual databases. Our system was initially developed in the MS-DOS environment, running ETEN Chinese operating system. For performance reasons, it was then tested on a UNIX-based system. Due to the unique ideographic nature of the Chinese language, a Chinese term-phrase indexing paradigm considering the ideographic characteristics of Chinese was developed as a multilingual information classification model. By applying the neural network based concept classification technique, the model presents a novel way of organizing unstructured multilingual information View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A relationship between membership functions and approximation accuracy in fuzzy systems

    Page(s): 176 - 180
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (480 KB)  

    This paper presents a relationship between membership functions and approximation accuracy in fuzzy systems. This relationship suggests an idea to design membership functions such that the approximation accuracy of fuzzy systems is improved View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Convergence acceleration of the Hopfield neural network by optimizing integration step sizes

    Page(s): 194 - 201
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (664 KB)  

    In our previous work we have clarified global convergence of the Hopfield neural network and showed, by computer simulations, improvement of solution quality by gradually decreasing the diagonal elements of the coefficient matrix. In this paper, to accelerate convergence of the Hopfield network, at each time step the integration step size is determined dynamically so that at least one component of a variable vector reaches the surface of the hypercube. The computer simulation for the traveling salesman problem and an LSI module placement problem shows that convergence is stabilized and accelerated compared to integration by a constant step size View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Connectionist modeling for arm kinematics using visual information

    Page(s): 89 - 99
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1156 KB)  

    The self-organizing adaptive map algorithm is adopted to learn all possible postures for an artificial arm of arbitrary configuration placed in a three-dimensional workspace. Arm postures are represented through their projections onto a set of image planes. Based on the link orientation and link length extracted from these images, a topological state space Q is generated. Arm kinematics is expressed as a transformation of topological hypersurfaces, the intersections of which represents the multiple postures of the arm in the workspace for a given end effector position. The self-organizing feature map learns how the topological hypersurfaces transform in the state space during arbitrary movements of the arm in the workspace. During the learning phase, the neural network generates clusters of neurons, each neuron being responsible for reproducing an arm posture in the workspace. The neural clusters map the hypersurfaces' intersection in the topological Q-space to any position of the arm gripper in the workspace. Simulations for planar and nonplanar multiple degrees of freedom arms are presented View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Extension of the modified-histogramming method for multilevel Markov random fields

    Page(s): 180 - 187
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (872 KB)  

    A very useful method for the estimation of Markov random field (MRF) parameters has been proposed by H. Derin and H. Elliott (1987). A bias reduction based modification of that method, called the modified-histogramming (MH) method, has been proposed by M.I. Gurelli and L. Onural (1994) for binary MRFs mainly to obtain better performance in the case of small amounts of image data. In this correspondence, an extension of the MH method for multilevel MRFs is proposed. Also, a data merging technique is given to increase the performance of the method View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Fuzzy systems with defuzzification are universal approximators

    Page(s): 149 - 152
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (408 KB)  

    In this paper, we consider a fundamental theoretical question: Is it always possible to design a fuzzy system capable of approximating any real continuous function on a compact set with arbitrary accuracy? Moreover, we research whether the answer to the above question is positive when we restrict to a fixed (but arbitrary) type of fuzzy reasoning and to a subclass of fuzzy relations. This result can be viewed as an existence theorem of an optimal fuzzy system for a wide variety of problems View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Stability analysis of fuzzy control systems

    Page(s): 201 - 204
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (304 KB)  

    A discrete-time fuzzy control system which is composed of a dynamic fuzzy model and a fuzzy state feedback controller is proposed. Stability of the fuzzy control system is discussed and a sufficient condition to guarantee the stability of the system is given in terms of uncertain linear system theory. The results in this paper improve our previous stability results. An example is used to show the proposed method View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Learning templates from fuzzy examples in structural pattern recognition

    Page(s): 118 - 123
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (568 KB)  

    Fuzzy-Attribute Graph (FAG) was proposed to handle fuzziness in the pattern primitives in structural pattern recognition. FAG has the advantage that we can combine several possible definitions into a single template, and hence only one matching is required instead of one for each definition. Also, each vertex or edge of the graph can contain fuzzy attributes to model real-life situations. However, in our previous approach, we need a human expert to define the templates for the fuzzy graph matching. This is usually tedious, time-consuming and error-prone. In this paper, we propose a learning algorithm that will, from a number of fuzzy examples, each of them being a FAG, find the smallest template that can be matched to the given patterns with respect to the matching metric View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Extensions to the fuzzy pointed set with applications to image processing

    Page(s): 21 - 28
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (768 KB)  

    In all man-machine systems with image processing functions, an important unsolved problem arises in the treatment of uncertain and incomplete image information. Several frameworks have been suggested for handling uncertain image information including; expert systems, fuzzification, likelihood estimation, and neural networks. In this paper we review those methods. We also present a new method for handling uncertainties by unifying the representations of gray-values and uncertainty into one framework in a way that parallels fuzzy logic. This new framework is based on the application of the extended fuzzy pointed set and an associated algebra to handle uncertain information. We further show how this framework can be used in image processing and artificial intelligence View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Approximation properties of fuzzy systems generated by the min inference

    Page(s): 187 - 193
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (572 KB)  

    This paper discusses the approximation properties of fuzzy systems generated by the min inference. Firstly, the paper analyzes the properties of fuzzy basis functions (FBFs); Then based on the properties of FBPs, several basic approximation properties concerning approximation mechanisms, uniform approximation bounds, uniform convergency, and universal approximation are obtained. Further, the similarity and difference between the fuzzy systems generated by the product inference and by the min inference are discussed View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Enhancing MLP networks using a distributed data representation

    Page(s): 143 - 149
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (720 KB)  

    Multilayer perceptron (MLP) networks trained using backpropagation can be slow to converge in many instances. The primary reason for slow learning is the global nature of backpropagation. Another reason is the fact that a neuron in an MLP network functions as a hyperplane separator and is therefore inefficient when applied to classification problems in which decision boundaries are nonlinear. This paper presents a data representational approach that addresses these problems while operating within the framework of the familiar backpropagation model. We examine the use of receptors with overlapping receptive fields as a preprocessing technique for encoding inputs to MLP networks. The proposed data representation scheme, termed ensemble encoding, is shown to promote local learning and to provide enhanced nonlinear separability. Simulation results for well known problems in classification and time-series prediction indicate that the use of ensemble encoding can significantly reduce the time required to train MLP networks. Since the choice of representation for input data is independent of the learning algorithm and the functional form employed in the MLP model, nonlinear preprocessing of network inputs may be an attractive alternative for many MLP network applications View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Multiple response learning automata

    Page(s): 153 - 156
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (364 KB)  

    Learning Automata update their action probabilites on the basis of the response they get from a random environment. They use a reward adaptation rate for a favorable environment's response and a penalty adaptation rate for an unfavorable environment's response. In this correspondence, we introduce Multiple Response learning automata by explicitly classifying the environment responses into a reward (favorable) set and a penalty (unfavorable) set. We derive a new reinforcement scheme which uses different reward or penalty rates for the corresponding reward (favorable) or penalty (unfavorable) responses. Well known learning automata, such as the LR-P;LR-I; LR-eP are special cases of these Multiple Response learning automata. These automata are feasible at each step, nonabsorbing (when the penalty functions are positive), and strictly distance diminishing. Finally, we provide conditions in order that they are ergodic and expedient View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Dynamically focused fuzzy learning control

    Page(s): 53 - 74
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2388 KB)  

    A “learning system” possesses the capability to improve its performance over time by interacting with its environment. A learning control system is designed so that its “learning controller” has the ability to improve the performance of the closed-loop system by generating command inputs to the plant and utilizing feedback information from the plant. Learning controllers are often designed to mimic the manner in which a human in the control loop would learn how to control a system while it operates. Some characteristics of this human learning process may include: (i) a natural tendency for the human to focus their learning by paying particular attention to the current operating conditions of the system since these may be most relevant to determining how to enhance performance; (ii) after learning how to control the plant for some operating condition, if the operating conditions change, then the best way to control the system may have to be re-learned; and (iii) a human with a significant amount of experience at controlling the system in one operating region should not forget this experience if the operating condition changes. To mimic these types of human learning behavior, we introduce three strategies that can be used to dynamically focus a learning controller onto the current operating region of the system. We show how the subsequent “dynamically focused learning” (DFL) can be used to enhance the performance of the “fuzzy model reference learning controller” (FMRLC) and furthermore we perform comparative analysis with a conventional adaptive control technique. A magnetic ball suspension system is used throughout the paper to perform the comparative analyses, and to illustrate the concept of dynamically focused fuzzy learning control View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A distributed probabilistic system for adaptive regulation of image processing parameters

    Page(s): 1 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2972 KB)  

    A distributed optimization framework and its application to the regulation of the behavior of a network of interacting image processing algorithms are presented. The algorithm parameters used to regulate information extraction are explicitly represented as state variables associated with all network nodes. Nodes are also provided with message-passing procedures to represent dependences between parameter settings at adjacent levels. The regulation problem is defined as a joint-probability maximization of a conditional probabilistic measure evaluated over the space of possible configurations of the whole set of state variables (i.e., parameters). The global optimization problem is partitioned and solved in a distributed way, by considering local probabilistic measures for selecting and estimating the parameters related to specific algorithms used within the network. The problem representation allows a spatially varying tuning of parameters, depending on the different informative contents of the subareas of an image. An application of the proposed approach to an image processing problem is described. The processing chain chosen as an example consists of four modules. The first three algorithms correspond to network nodes. The topmost node is devoted to integrating information derived from applying different parameter settings to the algorithms of the chain. The nodes associated with data-transformation processes to be regulated are represented by an optical sensor and two filtering units (for edge-preserving and edge-extracting filterings), and a straight-segment detection module is used as an integration site View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Hierarchical time-extended Petri nets (H-EPNs) based error identification and recovery for multilevel systems

    Page(s): 164 - 175
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1072 KB)  

    In this paper, hierarchical time-extended Petri nets (H-EPNs), an extended Petri net based modeling and analysis tool, are used to derive the coordination level model of hierarchically decomposable systems, viewed from a three-level hierarchical structure of organization, coordination and execution of tasks. A two-layer (vertical) coordination level framework, consisting of the dispatcher/analyzer and the H-EPN controller is presented. A detailed two sub-level (horizontal) H-EPN controller model is derived to model system operations (including system soft failures). Error classification based on the interaction between the various system coordinators is derived from the H-EPN model. The H-EPN approach preserves multi-resolutional system details as well as effective communication flows between the various subsystems. A simple example illustrates the proposed approach. The important H-EPN properties of boundedness, safeness and reversibility are verified View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Complex systems modeling via fuzzy logic

    Page(s): 100 - 106
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (604 KB)  

    This paper presents a fuzzy logic approach to complex systems modeling that is based on fuzzy discretization technique. As compared with other modeling methods (both statistical and fuzzy), the proposed approach has the advantages of simplicity, flexibility, and high accuracy. Further, it is easy to use and may be handled by an automatic procedure. Numerical examples are provided to illustrate the performance of the proposed approach View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Early detection of independent motion from active control of normal image flow patterns

    Page(s): 42 - 52
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1356 KB)  

    An important initial step in interpreting a dynamic scene is to detect moving objects in the environment. This paper presents a novel solution to the problem of early motion detection by a moving observer. The solution requires the observer to be active in the acquisition of images thereby controlling the optical flow pattern due to egomotion. A theoretical analysis is done based on geometric considerations to establish conditions that are necessary and sufficient to guarantee motion detection at a point. The detection problem is posed in terms of locally computable image quantities (the normal image flow) which this makes it implementable in real time. The performance of the technique can be improved by imposing any applicable constraint; this is demonstrated for the detection of the motions of “compact” objects satisfying a size bound. The goal is to design a flexible and efficient early motion detection strategy that can be tailored to the needs of a particular navigation system View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Vector order statistics operators as color edge detectors

    Page(s): 135 - 143
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1172 KB)  

    Color edge detection is approached in this paper using vector order statistics. Based on the R-ordering method, a class of color edge detectors is defined. These detectors function as vector operators as opposed to component-wise operators. Specific edge detectors can be obtained as special cases of this class. Various such detectors are defined and analyzed. Experimental results show the noise robustness of the vector order statistics operators. A quantitative evaluation and comparison to other color edge detectors favors our approach. Edge detection results obtained from real color images demonstrate the effectiveness of the proposed approach in real applications View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Petri net modelling of buffers in automated manufacturing systems

    Page(s): 157 - 164
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (772 KB)  

    This paper presents Petri net models of buffers and a methodology by which buffers can be included in a system without introducing deadlocks or overflows. The context is automated manufacturing. The buffers and models are classified as random order or order preserved (first-in-first-out or last-in-first-out), single-input-single-output or multiple-input-multiple-output, part type and/or space distinguishable or indistinguishable, and bounded or safe. Theoretical results for the development of Petri net models which include buffer modules are developed. This theory provides the conditions under which the system properties of boundedness, liveness, and reversibility are preserved. The results are illustrated through two manufacturing system examples: a multiple machine and multiple buffer production line and an automatic storage and retrieval system in the context of flexible manufacturing View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Nonlinear system identification using a Gabor/Hopfield network

    Page(s): 124 - 134
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (820 KB)  

    This paper presents a method of nonlinear system identification using a new Gabor/Hopfield network. The network can identify nonlinear discrete-time models that are affine linear in the control. The system need not be asymptotically stable but must be bounded-input-bounded-output (BIBO) stable for the identification results to be valid in a large input-output range. The network is a considerable improvement over earlier work using Gabor basis functions (GBF's) with a back-propagation neural network. Properties of the Gabor model and guidelines for achieving a global error minimum are derived. The new network and its use in system identification are investigated through computer simulation. Practical problems such as local minima, the effects of input and initial conditions, the model sensitivity to noise, the sensitivity of the mean square error (MSE) to the number of basis functions and the order of approximation, and the choice of forcing function for training data generation are considered View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Reorganizing knowledge in neural networks: an explanatory mechanism for neural networks in data classification problems

    Page(s): 107 - 117
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1064 KB)  

    We propose an explanatory mechanism for multilayered neural networks (NN). In spite of the effective learning capability as a uniform function approximator, the multilayered NN suffers from unreadability, i.e., it is difficult for the user to interpret or understand the “knowledge” that the NN has by looking at the connection weights and thresholds obtained by backpropagation (BP). This unreadability comes from the distributed nature of the knowledge representation in the NN. In this paper, we propose a method that reorganizes the distributed knowledge in the NN to extract approximate classification rules. Our rule extraction method is based on the analysis of the function that the NN has learned, rather than on the direct interpretation of connection weights as correlation information. More specifically, our method divides the input space into “monotonic regions” where a monotonic region is a set of input patterns that belongs to the same class with the same sensitivity pattern. Approximate classification rules are generated by projecting these monotonic regions View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

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