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

Issue 1 • Date Feb 2003

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Displaying Results 1 - 17 of 17
  • A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance

    Page(s): 17 - 27
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1017 KB)  

    Fuzzy logic systems are promising for efficient obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. A reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs a heavy learning phase and may result in an insufficiently learned rule base due to the curse of dimensionality. In this paper, we propose a neural fuzzy system with mixed coarse learning and fine learning phases. In the first phase, a supervised learning method is used to determine the membership functions for input and output variables simultaneously. After sufficient training, fine learning is applied which employs reinforcement learning algorithm to fine-tune the membership functions for output variables. For sufficient learning, a new learning method using a modification of Sutton and Barto's model is proposed to strengthen the exploration. Through this two-step tuning approach, the mobile robot is able to perform collision-free navigation. To deal with the difficulty of acquiring a large amount of training data with high consistency for supervised learning, we develop a virtual environment (VE) simulator, which is able to provide desktop virtual environment (DVE) and immersive virtual environment (IVE) visualization. Through operating a mobile robot in the virtual environment (DVE/IVE) by a skilled human operator, training data are readily obtained and used to train the neural fuzzy system. View full abstract»

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  • Regularization parameter estimation for feedforward neural networks

    Page(s): 35 - 44
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (757 KB)  

    Under the framework of the Kullback-Leibler (KL) distance, we show that a particular case of Gaussian probability function for feedforward neural networks (NNs) reduces into the first-order Tikhonov regularizer. The smooth parameter in kernel density estimation plays the role of regularization parameter. Under some approximations, an estimation formula is derived for estimating regularization parameters based on training data sets. The similarity and difference of the obtained results are compared with other work. Experimental results show that the estimation formula works well in sparse and small training sample cases. View full abstract»

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  • Visualization of learning in multilayer perceptron networks using principal component analysis

    Page(s): 28 - 34
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (333 KB) |  | HTML iconHTML  

    This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as backpropagation and can also be used to provide insight into the learning process and the nature of the error surface. View full abstract»

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  • Model generation by domain refinement and rule reduction

    Page(s): 45 - 55
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1029 KB) |  | HTML iconHTML  

    The granularity and interpretability of a fuzzy model are influenced by the method used to construct the rule base. Models obtained by a heuristic assessment of the underlying system are generally highly granular with interpretable rules, while models algorithmically generated from an analysis of training data consist of a large number of rules with small granularity. This paper presents a method for increasing the granularity of rules while satisfying a prescribed precision bound on the training data. The model is generated by a two-stage process. The first step iteratively refines the partitions of the input domains until a rule base is generated that satisfies the precision bound. In this step, the antecedents of the rules are obtained from decomposable partitions of the input domains and the consequents are generated using proximity techniques. A greedy merging algorithm is then applied to increase the granularity of the rules while preserving the precision bound. To enhance the representational capabilities of a rule and reduce the number of rules required, the rules constructed by the merging procedure have multi-dimensional antecedents. A model defined with rules of this form incorporates advantageous features of both clustering and proximity methods for rule generation. Experimental results demonstrate the ability of the algorithm to reduce the number of rules in a fuzzy model with both precise and imprecise training information. View full abstract»

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  • Effective detection of coupling in short and noisy bivariate data

    Page(s): 85 - 95
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (653 KB)  

    In the study of complex systems, one of the primary concerns is the characterization and quantification of interdependencies between different subsystems. In real-life systems, the nature of dependencies or coupling can be nonlinear and asymmetric, rendering the classical linear methods unsuitable for this purpose. Furthermore, experimental signals are noisy and short, which pose additional constraints for the measurement of underlying coupling. We discuss an index based on nonlinear dynamical system theory to measure the degree of coupling which can be asymmetric. The usefulness of this index has been demonstrated by several examples including simulated and real-life signals. This index is found to effectively disclose the nature and the degree of interactions even when the coupling is very weak and data are noisy and of limited length; by this way, new insight into the functioning of the underlying complex system is possible. View full abstract»

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  • Entropy-Boltzmann selection in the genetic algorithms

    Page(s): 138 - 149
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (269 KB)  

    A new selection method, entropy-Boltzmann selection, for genetic algorithms (GAs) is proposed. This selection method is based on entropy and importance sampling methods in Monte Carlo simulation. It naturally leads to adaptive fitness in which the fitness function does not stay fixed but varies with the environment. With the selection method, the algorithm can explore as many configurations as possible while exploiting better configurations, consequently helping to solve the premature convergence problem. To test the performance of the selection method, we use the NK-model and compared the performances of the proposed selection scheme with those of canonical GAs. View full abstract»

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  • Recursive information granulation: aggregation and interpretation issues

    Page(s): 96 - 112
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (781 KB) |  | HTML iconHTML  

    This paper contributes to the conceptual and algorithmic framework of information granulation. We revisit the role of information granules that are relevant to several main classes of technical pursuits involving temporal and spatial granulation. A detailed algorithm of information granulation, regarded as an optimization problem reconciling two conflicting design criteria, namely, a specificity of information granules and their experimental relevance (coverage of numeric data), is provided in the paper. The resulting information granules are formalized in the language of set theory (interval analysis). The uniform treatment of data points and data intervals (sets) allows for a recursive application of the algorithm. We assess the quality of information granules through application of the fuzzy c-means (FCM) clustering algorithm. Numerical studies deal with two-dimensional (2D) synthetic data and experimental traffic data. View full abstract»

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  • Adaptive control for a class of second-order nonlinear systems with unknown input nonlinearities

    Page(s): 143 - 149
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (427 KB)  

    An adaptive controller is developed for a class of second-order nonlinear dynamic systems with input nonlinearities using artificial neural networks (ANN). The unknown input nonlinearities are continuous and monotone and satisfy a sector constraint. In contrast to conventional Lyapunov-based design techniques, an alternative Lyapunov function, which depends on both system states and control input variable, is used for the development of a control law and a learning algorithm. The proposed adaptive controller guarantees the stability of the closed-loop system and convergence of the output tracking error to an adjustable neighbour of the origin. View full abstract»

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  • Pattern classification using fuzzy relational calculus

    Page(s): 1 - 16
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1307 KB)  

    Our aim is to design a pattern classifier using fuzzy relational calculus (FRC) which was initially proposed by Pedrycz (1990). In the course of doing so, we first consider a particular interpretation of the multidimensional fuzzy implication (MFI) to represent our knowledge about the training data set. Subsequently, we introduce the notion of a fuzzy pattern vector to represent a population of training patterns in the pattern space and to denote the antecedent part of the said particular interpretation of the MFI. We introduce a new approach to the computation of the derivative of the fuzzy max-function and min-function using the concept of a generalized function. During the construction of the classifier based on FRC, we use fuzzy linguistic statements (or fuzzy membership function to represent the linguistic statement) to represent the values of features (e.g., feature F1 is small and F2 is big) for a population of patterns. Note that the construction of the classifier essentially depends on the estimate of a fuzzy relation ℜ between the input (fuzzy set) and output (fuzzy set) of the classifier. Once the classifier is constructed, the nonfuzzy features of a pattern can be classified. At the time of classification of the nonfuzzy features of the testpatterns, we use the concept of fuzzy masking to fuzzify the nonfuzzy feature values of the testpatterns. The performance of the proposed scheme is tested on synthetic data. Finally, we use the proposed scheme for the vowel classification problem of an Indian language. View full abstract»

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  • Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics

    Page(s): 113 - 121
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (584 KB) |  | HTML iconHTML  

    A general weapon-target assignment (WTA) problem is to find a proper assignment of weapons to targets with the objective of minimizing the expected damage of own-force asset. Genetic algorithms (GAs) are widely used for solving complicated optimization problems, such as WTA problems. In this paper, a novel GA with greedy eugenics is proposed. Eugenics is a process of improving the quality of offspring. The proposed algorithm is to enhance the performance of GAs by introducing a greedy reformation scheme so as to have locally optimal offspring. This algorithm is successfully applied to general WTA problems. From our simulations for those tested problems, the proposed algorithm has the best performance when compared to other existing search algorithms. View full abstract»

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  • Perceptually stable regions for arbitrary polygons

    Page(s): 165 - 171
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (537 KB) |  | HTML iconHTML  

    Zou and Yan have recently developed a skeletonization algorithm of digital shapes based on a regularity/singularity analysis; they use the polygon whose vertices are the boundary pixels of the image to compute a constrained Delaunay triangulation (CDT) in order to find local symmetries and stable regions. Their method has produced good results but it is slow since its complexity depends on the number of contour pixels. This paper presents an extension of their technique to handle arbitrary polygons, not only polygons of short edges. Consequently, not only can we achieve results as good as theirs for digital images, but we can also compute skeletons of polygons of any number of edges. Since we can handle polygonal approximations of figures, the skeletons are more resilient to noise and faster to process. View full abstract»

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  • Reduction of placement problems using Minkowski decomposition

    Page(s): 133 - 138
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (379 KB) |  | HTML iconHTML  

    The problem of finding collision-free placements for an object amid obstacles has two well-known solutions: the task space approach and the configuration space approach. In this correspondence, we study the mathematical structure of the placement problem, and show that Minkowski decomposition of the object produces a hierarchy of intermediate reformulations. This provides the mathematical foundation for common approximate solution methods already used in applications. In particular, it provides a recipe for discretizing rotations consistently. The methods discussed are particularly effective for simple shapes. View full abstract»

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  • Fuzzy neural network quadratic stabilization output feedback control for biped robots via H approach

    Page(s): 67 - 84
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1261 KB) |  | HTML iconHTML  

    A novel fuzzy neural network (FNN) quadratic stabilization output feedback control scheme is proposed for the trajectory tracking problems of biped robots with an FNN nonlinear observer. First, a robust quadratic stabilization FNN nonlinear observer is presented to estimate the joint velocities of a biped robot, in which an H approach and variable structure control (VSC) are embedded to attenuate the effect of external disturbances and parametric uncertainties. After the construction of the FNN nonlinear observer, a quadratic stabilization FNN controller is developed with a robust hybrid control scheme. As the employment of a quadratic stability approach, not only does it afford the possibility of trading off the design between FNN, H optimal control, and VSC, but conservative estimation of the FNN reconstruction error bound is also avoided by considering the system matrix uncertainty separately. It is shown that all signals in the closed-loop control system are bounded. View full abstract»

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  • Structural pattern recognition using genetic algorithms with specialized operators

    Page(s): 156 - 165
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (696 KB) |  | HTML iconHTML  

    This paper presents a genetic algorithm (GA)-based optimization procedure for structural pattern recognition in a model-based recognition system using attributed relational graph (ARG) matching technique. The objective of our work is to improve the GA-based ARG matching procedures leading to a faster convergence rate and better quality mapping between a scene ARG and a set of given model ARGs. In this study, potential solutions are represented by integer strings indicating the mapping between scene and model vertices. The fitness of each solution string is computed by accumulating the similarity between the unary and binary attributes of the matched vertex pairs. We propose novel crossover and mutation operators, specifically for this problem. With these specialized genetic operators, the proposed algorithm converges to better quality solutions at a faster rate than the standard genetic algorithm (SGA). In addition, the proposed algorithm is also capable of recognizing multiple instances of any model object. An efficient pose-clustering algorithm is used to eliminate occasional wrong mappings and to determine the presence/pose of the model in the scene. We demonstrate the superior performance of our proposed algorithm using extensive experimental results. View full abstract»

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  • Learning-based resource optimization in asynchronous transfer mode (ATM) networks

    Page(s): 122 - 132
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (522 KB) |  | HTML iconHTML  

    This paper tackles the issue of bandwidth allocation in asynchronous transfer mode (ATM) networks using recently developed tools of computational intelligence. The efficient bandwidth allocation technique implies effective resources utilization of the network. The fluid flow model has been used effectively among other conventional techniques to estimate the bandwidth for a set of connections. However, such methods have been proven to be inefficient at times in coping with varying and conflicting bandwidth requirements of the different services in ATM networks. This inefficiency is due to the computational complexity of the model. To overcome this difficulty, many approximation-based solutions, such as the fluid flow approximation technique, were introduced. Although such solutions are simple, in terms of computational complexity, they nevertheless suffer from potential inaccuracies in estimating the required bandwidth. Soft computing-based bandwidth controllers, such as neural networks- and neurofuzzy-based controllers, have been shown to effectively solve an indeterminate nonlinear input-output (I-O) relations by learning from examples. Applying these techniques to the bandwidth allocation problem in ATM network yields a flexible control mechanism that offers a fundamental tradeoff for the accuracy-simplicity dilemma. View full abstract»

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  • A linear matrix inequality (LMI) approach to robust H2 sampled-data control for linear uncertain systems

    Page(s): 149 - 155
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (426 KB) |  | HTML iconHTML  

    In this paper, we consider the H2 sampled-data control for uncertain linear systems by the impulse response interpretation of the H2 norm. Two H2 measures for sampled-data systems are considered. The robust optimal control procedures subject to these two H2 criteria are proposed. The development is primarily concerned with a multirate treatment in which a periodic time-varying robust optimal control for uncertain linear systems is presented. To facilitate multirate control design, a new result of stability of hybrid system is established. Moreover, the single-rate case is also obtained as a special case. The sampling period is explicitly involved in the result which is superior to traditional methods. The solution procedures proposed in this paper are formulated as an optimization problem subject to linear matrix inequalities. Finally, we present a numerical example to demonstrate the proposed techniques. View full abstract»

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  • New approach to intelligent control systems with self-exploring process

    Page(s): 56 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (691 KB)  

    This paper proposes an intelligent control system called self-exploring-based intelligent control system (SEICS). The SEICS is comprised of three basic mechanisms, namely, controller, performance evaluator (PE), and adaptor. The controller is constructed by a fuzzy neural network (FNN) to carry out the control tasks. The PE is used to determine whether or not the controller's performance is satisfactory. The adaptor, comprised of two elements, action explorer (AE) and rule generator (RG), plays the main role in the system for generating new control behaviors in order to enhance the control performance. AE operates through a three-stage self-exploration process to explore new actions, which is realized by the multiobjective genetic algorithm (GA). The RG transforms control actions to fuzzy rules based on a numerical method. The application of the adaptor can make a control system more adaptive in various environments. A simulation of robotic path-planning is used to demonstrate the proposed model. The results show that the robot reaches the target point from the start point successfully in the lack-of-information and changeable environments. 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.

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Meet Our Editors

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