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Neural Networks, IEEE Transactions on

Issue 1 • Date Jan. 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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  • Editorial: The IEEE Transactions on Neural Networks 2010 and Beyond

    Publication Year: 2010 , Page(s): 1 - 10
    Cited by:  Papers (1)
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  • Global Synchronization for Discrete-Time Stochastic Complex Networks With Randomly Occurred Nonlinearities and Mixed Time Delays

    Publication Year: 2010 , Page(s): 11 - 25
    Cited by:  Papers (108)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (902 KB) |  | HTML iconHTML  

    In this paper, the problem of stochastic synchronization analysis is investigated for a new array of coupled discrete-time stochastic complex networks with randomly occurred nonlinearities (RONs) and time delays. The discrete-time complex networks under consideration are subject to: (1) stochastic nonlinearities that occur according to the Bernoulli distributed white noise sequences; (2) stochasti... View full abstract»

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  • An Effective Method of Pruning Support Vector Machine Classifiers

    Publication Year: 2010 , Page(s): 26 - 38
    Cited by:  Papers (7)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1856 KB) |  | HTML iconHTML  

    Support vector machine (SVM) classifiers often contain many SVs, which lead to high computational cost at runtime and potential overfitting. In this paper, a practical and effective method of pruning SVM classifiers is systematically developed. The kernel row vectors, with one-to-one correspondence to the SVs, are first organized into clusters. The pruning work is divided into two phases. In the f... View full abstract»

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  • Global Asymptotic Stability of Reaction–Diffusion Cohen–Grossberg Neural Networks With Continuously Distributed Delays

    Publication Year: 2010 , Page(s): 39 - 49
    Cited by:  Papers (31)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (525 KB) |  | HTML iconHTML  

    This paper is concerned with the global asymptotic stability of a class of reaction-diffusion Cohen-Grossberg neural networks with continuously distributed delays. Under some suitable assumptions and using a matrix decomposition method, we apply the linear matrix inequality (LMI) method to propose some new sufficient stability conditions for the reaction-diffusion Cohen-Grossberg neural networks w... View full abstract»

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  • Output Feedback Control of a Quadrotor UAV Using Neural Networks

    Publication Year: 2010 , Page(s): 50 - 66
    Cited by:  Papers (53)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (763 KB) |  | HTML iconHTML  

    In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like a... View full abstract»

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  • Impulsive Control and Synchronization for Delayed Neural Networks With Reaction–Diffusion Terms

    Publication Year: 2010 , Page(s): 67 - 81
    Cited by:  Papers (29)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1310 KB) |  | HTML iconHTML  

    This paper discuss the global exponential stability and synchronization of the delayed reaction-diffusion neural networks with Dirichlet boundary conditions under the impulsive control in terms of p-norm and point out the fact that there is no constant equilibrium point other than the origin for the reaction-diffusion neural networks with Dirichlet boundary conditions. Some new and useful conditio... View full abstract»

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  • Blind Separation of Mutually Correlated Sources Using Precoders

    Publication Year: 2010 , Page(s): 82 - 90
    Cited by:  Papers (20)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (393 KB) |  | HTML iconHTML  

    This paper studies the problem of blind source separation (BSS) from instantaneous mixtures with the assumption that the source signals are mutually correlated. We propose a novel approach to BSS by using precoders in transmitters. We show that if the precoders are properly designed, some cross-correlation coefficients of the coded signals can be forced to be zero at certain time lags. Then, the u... View full abstract»

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  • Novel Weighting-Delay-Based Stability Criteria for Recurrent Neural Networks With Time-Varying Delay

    Publication Year: 2010 , Page(s): 91 - 106
    Cited by:  Papers (61)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (590 KB) |  | HTML iconHTML  

    In this paper, a weighting-delay-based method is developed for the study of the stability problem of a class of recurrent neural networks (RNNs) with time-varying delay. Different from previous results, the delay interval [0, d(t)] is divided into some variable subintervals by employing weighting delays. Thus, new delay-dependent stability criteria for RNNs with time-varying delay are derived by a... View full abstract»

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  • A Dirichlet Process Mixture of Generalized Dirichlet Distributions for Proportional Data Modeling

    Publication Year: 2010 , Page(s): 107 - 122
    Cited by:  Papers (13)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1720 KB) |  | HTML iconHTML  

    In this paper, we propose a clustering algorithm based on both Dirichlet processes and generalized Dirichlet distribution which has been shown to be very flexible for proportional data modeling. Our approach can be viewed as an extension of the finite generalized Dirichlet mixture model to the infinite case. The extension is based on nonparametric Bayesian analysis. This clustering algorithm does ... View full abstract»

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  • Relevance Units Latent Variable Model and Nonlinear Dimensionality Reduction

    Publication Year: 2010 , Page(s): 123 - 135
    Cited by:  Papers (3)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1354 KB) |  | HTML iconHTML  

    A new dimensionality reduction method, called relevance units latent variable model (RULVM), is proposed in this paper. RULVM has a close link with the framework of Gaussian process latent variable model (GPLVM) and it originates from a recently developed sparse kernel model called relevance units machine (RUM). RUM follows the idea of relevance vector machine (RVM) under the Bayesian framework bu... View full abstract»

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  • eFSM—A Novel Online Neural-Fuzzy Semantic Memory Model

    Publication Year: 2010 , Page(s): 136 - 157
    Cited by:  Papers (20)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1984 KB) |  | HTML iconHTML  

    Fuzzy rule-based systems (FRBSs) have been successfully applied to many areas. However, traditional fuzzy systems are often manually crafted, and their rule bases that represent the acquired knowledge are static and cannot be trained to improve the modeling performance. This subsequently leads to intensive research on the autonomous construction and tuning of a fuzzy system directly from the obser... View full abstract»

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  • OP-ELM: Optimally Pruned Extreme Learning Machine

    Publication Year: 2010 , Page(s): 158 - 162
    Cited by:  Papers (63)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (259 KB) |  | HTML iconHTML  

    In this brief, the optimally pruned extreme learning machine (OP-ELM) methodology is presented. It is based on the original extreme learning machine (ELM) algorithm with additional steps to make it more robust and generic. The whole methodology is presented in detail and then applied to several regression and classification problems. Results for both computational time and accuracy (mean square er... View full abstract»

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  • Feature Fusion Using Locally Linear Embedding for Classification

    Publication Year: 2010 , Page(s): 163 - 168
    Cited by:  Papers (8)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (300 KB) |  | HTML iconHTML  

    In most complex classification problems, many types of features have been captured or extracted. Feature fusion is used to combine features for better classification and to reduce data dimensionality. Kernel-based feature fusion methods are very effective for classification, but they do not reduce data dimensionality. In this brief, we propose an effective feature fusion method using locally linea... View full abstract»

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  • Exponential Stability on Stochastic Neural Networks With Discrete Interval and Distributed Delays

    Publication Year: 2010 , Page(s): 169 - 175
    Cited by:  Papers (48)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (273 KB) |  | HTML iconHTML  

    This brief addresses the stability analysis problem for stochastic neural networks (SNNs) with discrete interval and distributed time-varying delays. The interval time-varying delay is assumed to satisfy 0 < d1 ?? d(t) ?? d2 and is described as d(t) = d 1+h(t) with 0 ?? h(t) ?? d 2 - d ... View full abstract»

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  • On the Discrete-Time Dynamics of a Class of Self-Stabilizing MCA Extraction Algorithms

    Publication Year: 2010 , Page(s): 175 - 181
    Cited by:  Papers (6)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (293 KB) |  | HTML iconHTML  

    The minor component analysis (MCA) deals with the recovery of the eigenvector associated to the smallest eigenvalue of the autocorrelation matrix of the input dada, and it is a very important tool for signal processing and data analysis. This brief analyzes the convergence and stability of a class of self-stabilizing MCA algorithms via a deterministic discrete-time (DDT) method. Some sufficient co... View full abstract»

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  • Special issue on White Box Nonlinear Prediction Models

    Publication Year: 2010 , Page(s): 182
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  • 2010 IEEE World Congress on Computational Intelligence (WCCI)

    Publication Year: 2010 , Page(s): 183
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    Publication Year: 2010 , Page(s): 184
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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