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

Issue 6 • Date Dec. 1999

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  • Author index

    Publication Year: 1999 , Page(s): 1 - 4
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  • Subject index

    Publication Year: 1999 , Page(s): 4 - 10
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  • Linguistic models and linguistic modeling

    Publication Year: 1999 , Page(s): 745 - 757
    Cited by:  Papers (36)
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    The study is concerned with a linguistic approach to the design of a new category of fuzzy (granular) models. In contrast to numerically driven identification techniques, we concentrate on budding meaningful linguistic labels (granules) in the space of experimental data and forming the ensuing model as a web of associations between such granules. As such models are designed at the level of information granules and generate results in the same granular rather than pure numeric format, we refer to them as linguistic models. Furthermore, as there are no detailed numeric estimation procedures involved in the construction of the linguistic models carried out in this way, their design mode can be viewed as that of a rapid prototyping. The underlying algorithm used in the development of the models utilizes an augmented version of the clustering technique (context-based clustering) that is centered around a notion of linguistic contexts-a collection of fuzzy sets or fuzzy relations defined in the data space (more precisely a space of input variables). The detailed design algorithm is provided and contrasted with the standard modeling approaches commonly encountered in the literature. The usefulness of the linguistic mode of system modeling is discussed and illustrated with the aid of numeric studies including both synthetic data as well as some time series dealing with modeling traffic intensity over a broadband telecommunication network View full abstract»

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  • A survey of fuzzy clustering algorithms for pattern recognition. II

    Publication Year: 1999 , Page(s): 786 - 801
    Cited by:  Papers (46)
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    For pt.I see ibid., p.775-85. In part I an equivalence between the concepts of fuzzy clustering and soft competitive learning in clustering algorithms is proposed on the basis of the existing literature. Moreover, a set of functional attributes is selected for use as dictionary entries in the comparison of clustering algorithms. In this paper, five clustering algorithms taken from the literature are reviewed, assessed and compared on the basis of the selected properties of interest. These clustering models are (1) self-organizing map (SOM); (2) fuzzy learning vector quantization (FLVQ); (3) fuzzy adaptive resonance theory (fuzzy ART); (4) growing neural gas (GNG); (5) fully self-organizing simplified adaptive resonance theory (FOSART). Although our theoretical comparison is fairly simple, it yields observations that may appear parodoxical. First, only FLVQ, fuzzy ART, and FOSART exploit concepts derived from fuzzy set theory (e.g., relative and/or absolute fuzzy membership functions). Secondly, only SOM, FLVQ, GNG, and FOSART employ soft competitive learning mechanisms, which are affected by asymptotic misbehaviors in the case of FLVQ, i.e., only SOM, GNG, and FOSART are considered effective fuzzy clustering algorithms View full abstract»

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  • On generating FC3 fuzzy rule systems from data using evolution strategies

    Publication Year: 1999 , Page(s): 829 - 845
    Cited by:  Papers (52)
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    Sophisticated fuzzy rule systems are supposed to be flexible, complete, consistent and compact (FC3). Flexibility, and consistency are essential for fuzzy systems to exhibit an excellent performance and to have a clear physical meaning, while compactness is crucial when the number of the input variables increases. However, the completeness and consistency conditions are often violated if a fuzzy system is generated from data collected from real world applications. A systematic design paradigm is proposed using evolution strategies. The structure of the fuzzy rules, which determines the compactness of the fuzzy systems, is evolved along with the parameters of the fuzzy systems. Special attention has been paid to the completeness and consistency of the rule base. The completeness is guaranteed by checking the completeness of the fuzzy partitioning of input variables and the completeness of the rule structure. An index of inconsistency is suggested with the help of a fuzzy similarity which can prevent the algorithm from generating rules that seriously contradict with each other or with the heuristic knowledge. In addition, soft T-norm and BADD defuzzification are introduced and optimized to increase the flexibility of the fuzzy system. The proposed approach is applied to the design of a distance controller for cars. It is verified that a FC3 fuzzy system works very well both, for training and test driving situations, especially when the training data are insufficient View full abstract»

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  • Linearization and state estimation of unknown discrete-time nonlinear dynamic systems using recurrent neurofuzzy networks

    Publication Year: 1999 , Page(s): 802 - 817
    Cited by:  Papers (4)
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    Model-based methods for the state estimation and control of linear systems have been well developed and widely applied. In practice, the underlying systems are often unknown and nonlinear. Therefore, data based model identification and associated linearization techniques are very important. Local linearization and feedback linearization have drawn considerable attention in recent years. In this paper, linearization techniques using neural networks are reviewed, together with theoretical difficulties associated with the application of feedback linearization. A recurrent neurofuzzy network with an analysis of variance (ANOVA) decomposition structure and its learning algorithm are proposed for linearizing unknown discrete-time nonlinear dynamic systems. It can be viewed as a method for approximate feedback linearization, as such it enlarges the class of nonlinear systems that can be feedback linearized using neural networks. Applications of this new method to state estimation are investigated with realistic simulation examples, which shows that the new method has useful practical properties such as model parametric parsimony and learning convergence, and is effective in dealing with complex unknown nonlinear systems View full abstract»

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  • Adaptive neural network control of nonlinear systems by state and output feedback

    Publication Year: 1999 , Page(s): 818 - 828
    Cited by:  Papers (91)
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    This paper presents a novel control method for a general class of nonlinear systems using neural networks (NNs). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the output is measurable, by using a high-gain observer to estimate the derivatives of the system output, an adaptive output feedback NN controller is proposed. The closed-loop system is proven to be semi-globally uniformly ultimately bounded (SGUUB). In addition, if the approximation accuracy of the neural networks is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussions View full abstract»

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  • Derivation and analytic evaluation of an equivalence relation clustering algorithm

    Publication Year: 1999 , Page(s): 908 - 912
    Cited by:  Patents (1)
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    Clustering algorithms have been recently used in multitarget multisensor tracking (MMT) problems in order to reduce the size of the data association problem. This paper derives an equivalence relation (ER) clustering algorithm used in a MMT problem and briefly compares it to other clustering schemes such as the nearest neighbor method. The main contribution of this work is the analytical evaluation of ER clustering performance, in the context of multitarget multisensor tracking, as a function of the distance between targets, measurement probability density function, and cluster parameter View full abstract»

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  • POPFNN-AAR(S): a pseudo outer-product based fuzzy neural network

    Publication Year: 1999 , Page(s): 859 - 870
    Cited by:  Papers (32)
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    A novel fuzzy neural network, the pseudo outer-product-based fuzzy neural network using the singleton fuzzifier together with the approximate analogical reasoning schema, is proposed in this paper. The network is referred to as the singleton fuzzifier POPFNN-AARS, the singleton fuzzifier POPFNN-AARS employs the approximate analogical reasoning schema (AARS) instead of the commonly used truth value restriction (TVR) method. This makes the structure and learning algorithms of the singleton fuzzifier POPFNN-AARS simple and conceptually clearer than those of the POPFNN-TVR model. Different similarity measures (SM) and modification functions (FM) for AARS are investigated. The structures and learning algorithms of the proposed singleton fuzzifer POPFNN-AARS are presented. Several sets of real-life data are used to test the performance of the singleton fuzzifier POPFNN-AARS and their experimental results are presented for detailed discussion View full abstract»

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  • Simultaneous training of negatively correlated neural networks in an ensemble

    Publication Year: 1999 , Page(s): 716 - 725
    Cited by:  Papers (72)  |  Patents (4)
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    This paper presents a new cooperative ensemble learning system (CELS) for designing neural network ensembles. The idea behind CELS is to encourage different individual networks in an ensemble to learn different parts or aspects of a training data so that the ensemble can learn the whole training data better. In CELS, the individual networks are trained simultaneously rather than independently or sequentially. This provides an opportunity for the individual networks to interact with each other and to specialize. CELS can create negatively correlated neural networks using a correlation penalty term in the error function to encourage such specialization. This paper analyzes CELS in terms of bias-variance-covariance tradeoff. CELS has also been tested on the Mackey-Glass time series prediction problem and the Australian credit card assessment problem. The experimental results show that CELS can produce neural network ensembles with good generalization ability View full abstract»

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  • Fuzzy relation equations and fuzzy inference systems: an inside approach

    Publication Year: 1999 , Page(s): 694 - 702
    Cited by:  Papers (6)
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    This paper investigates and extends the use of fuzzy relation equations for the representation and study of fuzzy inference systems. Using the generalized sup-t (t is a triangular norm) composition of fuzzy relations and the study of sup-t fuzzy relation equations, interesting results are provided concerning the completeness and the theoretical soundness of the representation, as well as the ability to mathematically formulate and satisfy application-oriented design demands. Furthermore, giving a formal study of fuzzy partitions and some useful aspects of fuzzy associations and fuzzy systems, the paper can be used as a theoretical background for designing consistent fuzzy inference systems View full abstract»

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  • Clustering of symbolic objects using gravitational approach

    Publication Year: 1999 , Page(s): 888 - 894
    Cited by:  Papers (10)
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    Most of the techniques used in the literature in clustering symbolic data are based on the hierarchical methodology, which uses the concept of agglomeration or division as the core of the algorithm. The main contribution of this paper is to formulate a clustering algorithm for symbolic objects based on the gravitational approach. The proposed procedure is based on the physical phenomenon in which a system of particles in space converge to the centroid of the system due to gravitational attraction between the particles. Some pairs of samples called mutual pairs, which have a tendency to gravitate toward each other, are discerned at each stage of this multistage scheme. The notions of cluster coglomerate strength and global coglomerate strength are used for accomplishing or abandoning the process of merging a mutual pair. The methodology forms composite symbolic objects whenever two symbolic objects are merged. The process of merging at each stage, reduces the number of samples that are available for consideration. The procedure terminates at some stage where there are no more mutual pairs available for merging. The efficacy of the proposed methodology is examined by applying it on numeric data and also on data sets drawn from the domain of fat oil, microcomputers, microprocessors, and botany. A detailed comparative study is carried out with other methods and the results are presented View full abstract»

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  • A reinforcement neuro-fuzzy combiner for multiobjective control

    Publication Year: 1999 , Page(s): 726 - 744
    Cited by:  Papers (5)
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    This paper proposes a neuro-fuzzy combiner (NFC) with reinforcement learning capability for solving multiobjective control problems. The proposed NFC can combine n existing low-level controllers in a hierarchical way to form a multiobjective fuzzy controller. It is assumed that each low-level (fuzzy or nonfuzzy) controller has been well designed to serve a particular objective. The role of the NFC is to fuse the n actions decided by the n low-level controllers and determine a proper action acting on the environment (plant) at each time step. Hence, the NFC can combine low-level controllers and achieve multiple objectives (goals) at once. The NFC acts like a switch that chooses a proper action from the actions of low-level controllers according to the feedback information from the environment. In fact, the NFC is a soft switch; it allows more than one low-level actions to be active with different degrees through fuzzy combination at each time step. An NFC can be designed by the trial-and-error approach if enough a priori knowledge is available, or it can be obtained by supervised learning if precise input/output training data are available. In the more practical cases when there is no instructive teaching information available, the NFC can learn by itself using the proposed reinforcement learning scheme. Adopted with reinforcement learning capability, the NFC can learn to achieve desired multiobjectives simultaneously through the rough reinforcement feedback from the environment, which contains only critic information such as “success (good)” or “failure (bad)” for each desired objective. Computer simulations have been conducted to illustrate the performance and applicability of the proposed architecture and learning scheme View full abstract»

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  • Reactive navigation in dynamic environment using a multisensor predictor

    Publication Year: 1999 , Page(s): 870 - 880
    Cited by:  Papers (11)  |  Patents (1)
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    A reactive navigation system for an autonomous mobile robot in unstructured dynamic environments is presented. The motion of moving obstacles is estimated for robot motion planning and obstacle avoidance. A multisensor-based obstacle predictor is utilized to obtain obstacle-motion information. Sensory data from a CCD camera and multiple ultrasonic range finders are combined to predict obstacle positions at the next sampling instant. A neural network, which is trained off-line, provides the desired prediction on-line in real time. The predicted obstacle configuration is employed by the proposed virtual force based navigation method to prevent collision with moving obstacles. Simulation results are presented to verify the effectiveness of the proposed navigation system in an environment with multiple mobile robots or moving objects. This system was implemented and tested on an experimental mobile robot at our laboratory. Navigation results in real environment are presented and analyzed View full abstract»

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  • Designing syntactic pattern classifiers using vector quantization and parametric string editing

    Publication Year: 1999 , Page(s): 881 - 888
    Cited by:  Papers (1)
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    We consider a fundamental inference problem in syntactic pattern recognition (PR). We assume that the system has a dictionary which is a collection of all the ideal representations of the objects in question. To recognize a noisy sample, the system compares it with every element in the dictionary based on a nearest-neighbor philosophy, using three standard edit operations: substitution, insertion, and deletion, and the associated primitive elementary edit distances d(.,.). In this paper, we consider the assignment of the inter-symbol distances using the parametric distances. We show how the classifier can be trained to get the optimal parametric distance using vector quantization in the meta-space. In all our experiments, the training was typically achieved in a very few iterations. The subsequent classification accuracy we obtained using this single-parameter scheme was 96.13%. The power of the scheme is evident if we compare it to 96.67%, which is the accuracy of the scheme which uses the complete array of inter-symbol distances derived from a knowledge of all the confusion probabilities View full abstract»

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  • A survey of fuzzy clustering algorithms for pattern recognition. I

    Publication Year: 1999 , Page(s): 778 - 785
    Cited by:  Papers (89)
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    Clustering algorithms aim at modeling fuzzy (i.e., ambiguous) unlabeled patterns efficiently. Our goal is to propose a theoretical framework where the expressive power of clustering systems can be compared on the basis of a meaningful set of common functional features. Part I of this paper reviews the following issues related to clustering approaches found in the literature: relative (probabilistic) and absolute (possibilistic) fuzzy membership functions and their relationships to the Bayes rule, batch and on-line learning, prototype editing schemes, growing and pruning networks, modular network architectures, topologically perfect mapping, ecological nets and neuro-fuzziness. From this discussion an equivalence between the concepts of fuzzy clustering and soft competitive learning in clustering algorithms is proposed as a unifying framework in the comparison of clustering systems. Moreover, a set of functional attributes is selected for use as dictionary entries in the comparison of clustering algorithms, which is the subject of part II of this paper View full abstract»

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  • New techniques on deformed image motion estimation and compensation

    Publication Year: 1999 , Page(s): 846 - 859
    Cited by:  Papers (1)
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    In this paper, new techniques for deformed image motion estimation and compensation using variable-size block-matching are proposed, which can be applied to an image sequence compression system or a moving object recognition system. The motion estimation and compensation techniques have been successfully applied in the area of image sequence coding. Many research papers on improving the performance of these techniques have been published; many directions are proposed, which can all lead to better performance than the conventional techniques. Among them, both generalized block-matching and variable-size block-matching are successfully applied in reducing the data rate of compensation error and motion information, respectively. These two algorithms have their merits, but suffer from their drawbacks. Moreover, reducing the data rate in compensation error is sometimes increasing the data rate in motion information, or vice versa. Based on these two algorithms, we propose and examine several algorithms which are effective in reducing the data rate. We then incorporate these algorithms into a system, in which they work together to overcome the disadvantages to individual and keep their merits at the same time. The proposed system can optimally balance the amount of data rate in two aspects (i.e., compensation error and motion information). Experimental results show that the proposed system outweighs the conventional techniques. Since we propose a recovery operation which tries to recover the incorrect motion vectors from the global motion, this proposed system can also be applied to the moving object recognition in image sequences View full abstract»

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  • Adaptive classifier integration for robust pattern recognition

    Publication Year: 1999 , Page(s): 902 - 907
    Cited by:  Papers (14)  |  Patents (14)
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    The integration of multiple classifiers promises higher classification accuracy and robustness than can be obtained with a single classifier. This paper proposes a new adaptive technique for classifier integration based on a linear combination model. The proposed technique is shown to exhibit robustness to a mismatch between test and training conditions. It often outperforms the most accurate of the fused information sources. A comparison between adaptive linear combination and non-adaptive Bayesian fusion shows that, under mismatched test and training conditions, the former is superior to the latter in terms of identification accuracy and insensitivity to information source distortion View full abstract»

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  • A two-stage evolutionary process for designing TSK fuzzy rule-based systems

    Publication Year: 1999 , Page(s): 703 - 715
    Cited by:  Papers (35)
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    Nowadays, fuzzy rule-based systems are successfully applied to many different real-world problems. Unfortunately, relatively few well-structured methodologies exist for designing and, in many cases, human experts are not able to express the knowledge needed to solve the problem in the form of fuzzy rules. Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems were enunciated in order to solve this design problem because they are usually identified using numerical data. In this paper we present a two-stage evolutionary process for designing TSK fuzzy rule-based systems from examples combining a generation stage based on a (μ, λ)-evolution strategy, in which the fuzzy rules with different consequents compete among themselves to form part of a preliminary knowledge base, and a refinement stage in which both the antecedent and consequent parts of the fuzzy rules in this previous knowledge base are adapted by a hybrid evolutionary process composed of a genetic algorithm and an evolution strategy to obtain the final Knowledge base whose rules cooperate in the best possible way. Some aspects make this process different from others proposed until now: the design problem is addressed in two different stages, the use of an angular coding of the consequent parameters that allows us to search across the whole space of possible solutions, and the use of the available knowledge about the system under identification to generate the initial populations of the Evolutionary Algorithms that causes the search process to obtain good solutions more quickly. The performance of the method proposed is shown by solving two different problems: the fuzzy modeling of some three-dimensional surfaces and the computing of the maintenance costs of electrical medium line in Spanish towns. Results obtained are compared with other kind of techniques, evolutionary learning processes to design TSK and Mamdani-type fuzzy rule-based systems in the first case, and classical regression and neural modeling in the second View full abstract»

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  • A concept learning network based on correlation and backpropagation

    Publication Year: 1999 , Page(s): 912 - 916
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    A new concept learning neural network is presented. This network builds correlation learning into a rule learning neural network where the certainty factor model of traditional expert systems is taken as the network activation function. The main argument for this approach is that correlation learning can help when the neural network fails to converge to the target concept due to insufficient or noisy training data. Both theoretical analysis and empirical evaluation are provided to validate the system View full abstract»

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  • CAN: chain of nodes approach to direct rule induction

    Publication Year: 1999 , Page(s): 758 - 770
    Cited by:  Papers (4)
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    CAN is a heuristic algorithm that employs an information theoretic measure to learn rules. CAN approach distinguishes itself from other approaches by being direct, meaning that there are no intermediate representations, an induced rule is never altered in later stages and only tests that appear in the final solution are generated. In the selection of rule conditions (tests) existing rule induction algorithms do not provide a satisfactory answer to the partitioning of the feature space of discrete feature variables with nonordered qualitative values (i.e., categorical attributes) for multiclass problems. Existing algorithms have exponential complexity in N, where N is the number of feature values. Therefore, heuristic algorithms are employed at this step. An important contribution of this paper is to show that in test selection within CAN framework optimal partitions are achieved in linear time in N for the multiclass case View full abstract»

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  • A hybrid clustering and gradient descent approach for fuzzy modeling

    Publication Year: 1999 , Page(s): 686 - 693
    Cited by:  Papers (29)
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    In this paper, a hybrid clustering and gradient descent approach is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available. The proposed approach is composed of two steps: structure identification and parameter identification. In the process of structure identification, a clustering method is proposed to provide a systematic procedure to determine the number of fuzzy rules and construct an initial fuzzy model from the given input-output data. In the process of parameter identification, the gradient descent method is used to tune the parameters of the constructed fuzzy model to obtain a more precise fuzzy model from the given input-output data. Finally, two examples of nonlinear system are given to illustrate the effectiveness of the proposed approach View full abstract»

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  • Robust radial basis function neural networks

    Publication Year: 1999 , Page(s): 674 - 685
    Cited by:  Papers (34)  |  Patents (2)
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    Function approximation has been found in many applications. The radial basis function (RBF) network is one approach which has shown a great promise in this sort of problems because of its faster learning capacity. A traditional RBF network takes Gaussian functions as its basis functions and adopts the least-squares criterion as the objective function, However, it still suffers from two major problems. First, it is difficult to use Gaussian functions to approximate constant values. If a function has nearly constant values in some intervals, the RBF network will be found inefficient in approximating these values. Second, when the training patterns incur a large error, the network will interpolate these training patterns incorrectly. In order to cope with these problems, an RBF network is proposed in this paper which is based on sequences of sigmoidal functions and a robust objective function. The former replaces the Gaussian functions as the basis function of the network so that constant-valued functions can be approximated accurately by an RBF network, while the latter is used to restrain the influence of large errors. Compared with traditional RBF networks, the proposed network demonstrates the following advantages: (1) better capability of approximation to underlying functions; (2) faster learning speed; (3) better size of network; (4) high robustness to outliers View full abstract»

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  • Solving constraint satisfaction and optimization problems by a neuro-fuzzy approach

    Publication Year: 1999 , Page(s): 895 - 902
    Cited by:  Papers (2)
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    The solution of constrained satisfaction and constrained optimization problems using a Hopfield model requires determination of the values of a certain number of coefficients linked to the surrounding conditions of the problem. It is quite difficult to determine these values, mainly because a heuristic search is necessary. This is not only time-consuming but may lead to solutions that are far from optimal, or even nonvalid ones. So far, there have been no works in literature offering a general method for the search for coefficents with will guarantee optimal or close to optimal solutions. This paper proposes a fuzzy approach which allows automatic determination of Hopfield coefficients View full abstract»

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  • A global optimization method for nonlinear bilevel programming problems

    Publication Year: 1999 , Page(s): 771 - 777
    Cited by:  Papers (12)
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    Nonlinear two-level programming deals with optimization problems in which the constraint region is implicitly determined by another optimization problem. Mathematical programs of this type arise in connection with policy problems to which the Stackelberg leader-follower game is applicable. In this paper, the nonlinear bilevel programming problem is restated as a global optimization problem and a new solution method based on this approach is developed. The most important feature of this new method is that it attempts to take full advantage of the structure in the constraints using some recent global optimization techniques 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