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IEEE Transactions on Neural Networks

Issue 3 • Date March 2010

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Displaying Results 1 - 19 of 19
  • 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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  • PSECMAC Intelligent Insulin Schedule for Diabetic Blood Glucose Management Under Nonmeal Announcement

    Publication Year: 2010, Page(s):361 - 380
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2441 KB) | HTML iconHTML

    Therapeutically, the closed-loop blood glucose-insulin regulation paradigm via a controllable insulin pump offers a potential solution to the management of diabetes. However, the development of such a closed-loop regulatory system to date has been hampered by two main issues: 1) the limited knowledge on the complex human physiological process of glucose-insulin metabolism that prevents a precise m... View full abstract»

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  • A Very Fast Neural Learning for Classification Using Only New Incoming Datum

    Publication Year: 2010, Page(s):381 - 392
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1065 KB) | HTML iconHTML

    This paper proposes a very fast 1-pass-throw-away learning algorithm based on a hyperellipsoidal function that can be translated and rotated to cover the data set during learning process. The translation and rotation of hyperellipsoidal function depends upon the distribution of the data set. In addition, we present versatile elliptic basis function (VEBF) neural network with one hidden layer. The ... View full abstract»

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  • A Rank-One Update Algorithm for Fast Solving Kernel Foley–Sammon Optimal Discriminant Vectors

    Publication Year: 2010, Page(s):393 - 403
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (807 KB) | HTML iconHTML

    Discriminant analysis plays an important role in statistical pattern recognition. A popular method is the Foley-Sammon optimal discriminant vectors (FSODVs) method, which aims to find an optimal set of discriminant vectors that maximize the Fisher discriminant criterion under the orthogonal constraint. The FSODVs method outperforms the classic Fisher linear discriminant analysis (FLDA) method in t... View full abstract»

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  • A Model-Based Fault-Detection and Prediction Scheme for Nonlinear Multivariable Discrete-Time Systems With Asymptotic Stability Guarantees

    Publication Year: 2010, Page(s):404 - 423
    Cited by:  Papers (33)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (892 KB) | HTML iconHTML

    In this paper, a novel, unified model-based fault-detection and prediction (FDP) scheme is developed for nonlinear multiple-input-multiple-output (MIMO) discrete-time systems. The proposed scheme addresses both state and output faults by considering separate time profiles. The faults, which could be incipient or abrupt, are modeled using input and output signals of the system. The fault-detection ... View full abstract»

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  • Using Unsupervised Analysis to Constrain Generalization Bounds for Support Vector Classifiers

    Publication Year: 2010, Page(s):424 - 438
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1339 KB) | HTML iconHTML

    A crucial issue in designing learning machines is to select the correct model parameters. When the number of available samples is small, theoretical sample-based generalization bounds can prove effective, provided that they are tight and track the validation error correctly. The maximal discrepancy (MD) approach is a very promising technique for model selection for support vector machines (SVM), a... View full abstract»

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  • Computation of Synchronized Periodic Solution in a BAM Network With Two Delays

    Publication Year: 2010, Page(s):439 - 450
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (560 KB) | HTML iconHTML

    A bidirectional associative memory (BAM) neural network with four neurons and two discrete delays is considered to represent an analytical method, namely, perturbation-incremental scheme (PIS). The expressions for the periodic solutions derived from Hopf bifurcation are given by using the PIS. The result shows that the PIS has higher accuracy than the center manifold reduction (CMR) with normal fo... View full abstract»

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  • An Improved Algorithm for the Solution of the Regularization Path of Support Vector Machine

    Publication Year: 2010, Page(s):451 - 462
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (784 KB) | HTML iconHTML

    This paper describes an improved algorithm for the numerical solution to the support vector machine (SVM) classification problem for all values of the regularization parameter C . The algorithm is motivated by the work of Hastie and follows the main idea of tracking the optimality conditions of the SVM solution for ascending value of C . It differs from Hastie's approach in that the ... View full abstract»

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  • On the Weight Convergence of Elman Networks

    Publication Year: 2010, Page(s):463 - 480
    Cited by:  Papers (23)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1623 KB) | HTML iconHTML

    An Elman network (EN) can be viewed as a feedforward (FF) neural network with an additional set of inputs from the context layer (feedback from the hidden layer). Therefore, instead of the offline backpropagation-through-time (BPTT) algorithm, a standard online (real-time) backpropagation (BP) algorithm, usually called Elman BP (EBP), can be applied for EN training for discrete-time sequence predi... View full abstract»

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  • Coarse-to-Fine Boundary Location With a SOM-Like Method

    Publication Year: 2010, Page(s):481 - 493
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2604 KB) | HTML iconHTML

    A coarse-to-fine boundary location with a self-organizing map (SOM)-like method is proposed in this paper. Inspired from the conventional SOM and universal gravitation, given a small quantity of supervision seeds from the desired boundaries, neurons are used to evolve to the desired boundaries in a coarse-to-fine framework. The major components of this framework are the designs of union action and... View full abstract»

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  • Foundations of Implementing the Competitive Layer Model by Lotka–Volterra Recurrent Neural Networks

    Publication Year: 2010, Page(s):494 - 507
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (604 KB) | HTML iconHTML

    The competitive layer model (CLM) can be described by an optimization problem. The problem can be further formulated by an energy function, called the CLM energy function, in the subspace of nonnegative orthant. The set of minimum points of the CLM energy function forms the set of solutions of the CLM problem. Solving the CLM problem means to find out such solutions. Recurrent neural networks (RNN... View full abstract»

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  • Robust Stability Analysis for Stochastic Neural Networks With Time-Varying Delay

    Publication Year: 2010, Page(s):508 - 514
    Cited by:  Papers (24)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (297 KB) | HTML iconHTML

    This brief investigates the problem of mean square exponential stability of uncertain stochastic delayed neural networks (DNNs) with time-varying delay. A novel Lyapunov functional is introduced with the idea of the discretized Lyapunov-Krasovskii functional (LKF) method. Then, a new delay-dependent mean square exponential stability criterion is derived by applying the free-weighting matrix techni... View full abstract»

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  • Objective Image Quality Assessment Based on Support Vector Regression

    Publication Year: 2010, Page(s):515 - 519
    Cited by:  Papers (51)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1864 KB) | HTML iconHTML

    Objective image quality estimation is useful in many visual processing systems, and is difficult to perform in line with the human perception. The challenge lies in formulating effective features and fusing them into a single number to predict the quality score. In this brief, we propose a new approach to address the problem, with the use of singular vectors out of singular value decomposition (SV... View full abstract»

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  • Graph Based Representations of Density Distribution and Distances for Self-Organizing Maps

    Publication Year: 2010, Page(s):520 - 526
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1595 KB) | HTML iconHTML

    The self-organizing map (SOM) is a powerful method for manifold learning because of producing a 2-D spatially ordered quantization of a higher dimensional data space on a rigid lattice and adaptively determining optimal approximation of the (unknown) density distribution of the data. However, a postprocessing visualization scheme is often required to capture the data manifold. A recent visualizati... View full abstract»

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  • Special issue on white box nonlinear prediction models

    Publication Year: 2010, Page(s): 527
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    Publication Year: 2010, Page(s): 528
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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