IEEE Transactions on Neural Networks

Issue 4 • April 2010

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  • Table of contents

    Publication Year: 2010, Page(s): C1
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  • IEEE Transactions on Neural Networks publication information

    Publication Year: 2010, Page(s): C2
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  • An Adaptive Multiobjective Approach to Evolving ART Architectures

    Publication Year: 2010, Page(s):529 - 550
    Cited by:  Papers (22)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1226 KB) | HTML iconHTML

    In this paper, we present the evolution of adaptive resonance theory (ART) neural network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to simultaneously evolve the weights and the topology of three well-known ART architectures; fuzzy ARTMAP (FAM), ellipsoidal ARTMAP (EAM), and Gaussian ARTMAP (... View full abstract»

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  • Penalized Preimage Learning in Kernel Principal Component Analysis

    Publication Year: 2010, Page(s):551 - 570
    Cited by:  Papers (26)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (9766 KB) | HTML iconHTML

    Finding the preimage of a feature vector in kernel principal component analysis (KPCA) is of crucial importance when KPCA is applied in some applications such as image preprocessing. Since the exact preimage of a feature vector in the kernel feature space, normally, does not exist in the input data space, an approximate preimage is learned and encouraging results have been reported in the last few... View full abstract»

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  • Exponential Synchronization of Hybrid Coupled Networks With Delayed Coupling

    Publication Year: 2010, Page(s):571 - 583
    Cited by:  Papers (79)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1042 KB) | HTML iconHTML

    This paper investigates exponential synchronization of coupled networks with hybrid coupling, which is composed of constant coupling and discrete-delay coupling. There is only one transmittal delay in the delayed coupling. The fact is that in the signal transmission process, the time delay affects only the variable that is being transmitted from one system to another, then it makes sense to assume... View full abstract»

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  • State–Space Analysis of Boolean Networks

    Publication Year: 2010, Page(s):584 - 594
    Cited by:  Papers (59)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (378 KB) | HTML iconHTML

    This paper provides a comprehensive framework for the state-space approach to Boolean networks. First, it surveys the authors' recent work on the topic: Using semitensor product of matrices and the matrix expression of logic, the logical dynamic equations of Boolean (control) networks can be converted into standard discrete-time dynamics. To use the state-space approach, the state space and its su... View full abstract»

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  • Conformation-Based Hidden Markov Models: Application to Human Face Identification

    Publication Year: 2010, Page(s):595 - 608
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (807 KB) | HTML iconHTML

    Hidden Markov models (HMMs) and their variants are capable to classify complex and structured objects. However, one of their major restrictions is their inability to cope with shape or conformation intrinsically: HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the visible observation (VO) sequence. In order to fulfill this crucial need... View full abstract»

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  • Fast Vision Through Frameless Event-Based Sensing and Convolutional Processing: Application to Texture Recognition

    Publication Year: 2010, Page(s):609 - 620
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1968 KB) | HTML iconHTML

    Address-event representation (AER) is an emergent hardware technology which shows a high potential for providing in the near future a solid technological substrate for emulating brain-like processing structures. When used for vision, AER sensors and processors are not restricted to capturing and processing still image frames, as in commercial frame-based video technology, but sense and process vis... View full abstract»

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  • Generalized Low-Rank Approximations of Matrices Revisited

    Publication Year: 2010, Page(s):621 - 632
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (947 KB) | HTML iconHTML

    Compared to singular value decomposition (SVD), generalized low-rank approximations of matrices (GLRAM) can consume less computation time, obtain higher compression ratio, and yield competitive classification performance. GLRAM has been successfully applied to applications such as image compression and retrieval, and quite a few extensions have been successively proposed. However, in literature, s... View full abstract»

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  • Large-Scale Pattern Storage and Retrieval Using Generalized Brain-State-in-a-Box Neural Networks

    Publication Year: 2010, Page(s):633 - 643
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2449 KB) | HTML iconHTML

    In this paper, a generalized Brain-State-in-a-Box (gBSB)-based hybrid neural network is proposed for storing and retrieving pattern sequences. The hybrid network consists of autoassociative and heteroassociative parts. Then, a large-scale image storage and retrieval neural system is constructed using the gBSB-based hybrid neural network and the pattern decomposition concept. The notion of the dead... View full abstract»

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  • Simplifying Mixture Models Through Function Approximation

    Publication Year: 2010, Page(s):644 - 658
    Cited by:  Papers (33)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (864 KB) | HTML iconHTML

    The finite mixture model is widely used in various statistical learning problems. However, the model obtained may contain a large number of components, making it inefficient in practical applications. In this paper, we propose to simplify the mixture model by minimizing an upper bound of the approximation error between the original and the simplified model, under the use of the L 2 View full abstract»

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  • Boosting Through Optimization of Margin Distributions

    Publication Year: 2010, Page(s):659 - 666
    Cited by:  Papers (30)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (577 KB) | HTML iconHTML

    Boosting has been of great interest recently in the machine learning community because of the impressive performance for classification and regression problems. The success of boosting algorithms may be interpreted in terms of the margin theory. Recently, it has been shown that generalization error of classifiers can be obtained by explicitly taking the margin distribution of the training data int... View full abstract»

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  • Conic Section Function Neural Network Circuitry for Offline Signature Recognition

    Publication Year: 2010, Page(s):667 - 672
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (317 KB) | HTML iconHTML

    In this brief, conic section function neural network (CSFNN) circuitry was designed for offline signature recognition. CSFNN is a unified framework for multilayer perceptron (MLP) and radial basis function (RBF) networks to make simultaneous use of advantages of both. The CSFNN circuitry architecture was developed using a mixed mode circuit implementation. The designed circuit system is problem in... View full abstract»

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  • Black-Box Identification of a Class of Nonlinear Systems by a Recurrent Neurofuzzy Network

    Publication Year: 2010, Page(s):672 - 679
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1346 KB) | HTML iconHTML

    This brief presents a structure for black-box identification based on continuous-time recurrent neurofuzzy networks for a class of dynamic nonlinear systems. The proposed network catches the dynamics of a system by generating its own states, using only input and output measurements of the system. The training algorithm is based on adaptive observer theory, the stability of the network, the converg... View full abstract»

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  • Memory-Efficient Fully Coupled Filtering Approach for Observational Model Building

    Publication Year: 2010, Page(s):680 - 686
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (593 KB) | HTML iconHTML

    Generally, training neural networks with the global extended Kalman filter (GEKF) technique exhibits excellent performance at the expense of a large increase in computational costs which can become prohibitive even for networks of moderate size. This drawback was previously addressed by heuristically decoupling some of the weights of the networks. Inevitably, ad hoc decoupling leads to a de... View full abstract»

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  • Lattice Point Sets for Deterministic Learning and Approximate Optimization Problems

    Publication Year: 2010, Page(s):687 - 692
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (285 KB) | HTML iconHTML

    In this brief, the use of lattice point sets (LPSs) is investigated in the context of general learning problems (including function estimation and dynamic optimization), in the case where the classic empirical risk minimization (ERM) principle is considered and there is freedom to choose the sampling points of the input space. Here it is proved that convergence of the ERM principle is guaranteed w... View full abstract»

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  • Improved Delay-Dependent Stability Condition of Discrete Recurrent Neural Networks With Time-Varying Delays

    Publication Year: 2010, Page(s):692 - 697
    Cited by:  Papers (47)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (210 KB) | HTML iconHTML

    This brief investigates the problem of global exponential stability analysis for discrete recurrent neural networks with time-varying delays. In terms of linear matrix inequality (LMI) approach, a novel delay-dependent stability criterion is established for the considered recurrent neural networks via a new Lyapunov function. The obtained condition has less conservativeness and less number of vari... View full abstract»

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  • Special issue on white box nonlinear prediction models

    Publication Year: 2010, Page(s): 698
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  • 2010 IEEE World Congress on Computational Intelligence

    Publication Year: 2010, Page(s): 699
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  • Explore IEL IEEE's most comprehensive resource [advertisement]

    Publication Year: 2010, Page(s): 700
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2010, Page(s): C3
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  • IEEE Transactions on Neural Networks Information for authors

    Publication Year: 2010, Page(s): C4
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Aims & Scope

IEEE Transactions on Neural Networks is devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware.

 

This Transactions ceased production in 2011. The current retitled publication is IEEE Transactions on Neural Networks and Learning Systems.

Full Aims & Scope