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IEEE Transactions on Neural Networks

Issue 10 • Date Oct. 2009

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Displaying Results 1 - 21 of 21
  • Table of contents

    Publication Year: 2009, Page(s): C1
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  • IEEE Transactions on Neural Networks publication information

    Publication Year: 2009, Page(s): C2
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  • When Does Online BP Training Converge?

    Publication Year: 2009, Page(s):1529 - 1539
    Cited by:  Papers (19)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (438 KB) | HTML iconHTML

    The backpropogation (BP) neural networks have been widely applied in scientific research and engineering. The success of the application, however, relies upon the convergence of the training procedure involved in the neural network learning. We settle down the convergence analysis issue through proving two fundamental theorems on the convergence of the online BP training procedure. One theorem cla... View full abstract»

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  • Analysis and Modeling of Naturalness in Handwritten Characters

    Publication Year: 2009, Page(s):1540 - 1553
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1271 KB) | HTML iconHTML

    In this paper, we define the naturalness of handwritten characters as being the difference between the strokes of the handwritten characters and the archetypal fonts on which they are based. With this definition, we mathematically analyze the relationship between the font and its naturalness using canonical correlation analysis (CCA), multiple linear regression analysis, feedforward neural network... View full abstract»

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  • Privacy-Preserving Backpropagation Neural Network Learning

    Publication Year: 2009, Page(s):1554 - 1564
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (352 KB) | HTML iconHTML

    With the development of distributed computing environment , many learning problems now have to deal with distributed input data. To enhance cooperations in learning, it is important to address the privacy concern of each data holder by extending the privacy preservation notion to original learning algorithms. In this paper, we focus on preserving the privacy in an important learning model, multila... View full abstract»

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  • Accurate Estimation of ICA Weight Matrix by Implicit Constraint Imposition Using Lie Group

    Publication Year: 2009, Page(s):1565 - 1580
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3815 KB) | HTML iconHTML

    This paper presents a new stochastic algorithm to optimize the independence criterion-mutual information-among multivariate data using local, global, and hybrid optimizers, in conjunction with techniques involving a Lie group and its corresponding Lie algebra, for implicit imposition of the orthonormality constraint among the estimated sources. The major advantage of the proposed algorithm is the ... View full abstract»

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  • Multistability and New Attraction Basins of Almost-Periodic Solutions of Delayed Neural Networks

    Publication Year: 2009, Page(s):1581 - 1593
    Cited by:  Papers (47)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (866 KB) | HTML iconHTML

    In this paper, we investigate multistability of almost-periodic solutions of recurrently connected neural networks with delays (simply called delayed neural networks). We will reveal that under some conditions, the space Rn can be divided into 2n subsets, and in each subset, the delayed n -neuron neural network has a locally stable almost-periodic solution. Furthermore, we als... View full abstract»

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  • Semisupervised Multicategory Classification With Imperfect Model

    Publication Year: 2009, Page(s):1594 - 1603
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (384 KB) | HTML iconHTML

    Semisupervised learning has been of growing interest over the past years and many methods have been proposed. While existing semisupervised methods have shown some promising empirical performances, their development has been based largely on heuristics. In this paper, we investigate semisupervised multicategory classification with an imperfect mixture density model. In the proposed model, the trai... View full abstract»

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  • Granular Neural Networks and Their Development Through Context-Based Clustering and Adjustable Dimensionality of Receptive Fields

    Publication Year: 2009, Page(s):1604 - 1616
    Cited by:  Papers (25)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1493 KB) | HTML iconHTML

    In this study, we present a new architecture of a granular neural network and provide a comprehensive design methodology as well as elaborate on an algorithmic setup supporting its development. The proposed neural network relates to a broad category of radial basis function neural networks (RBFNNs) in the sense that its topology involves a collection of receptive fields. In contrast to the standar... View full abstract»

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  • Pinning Stabilization of Linearly Coupled Stochastic Neural Networks via Minimum Number of Controllers

    Publication Year: 2009, Page(s):1617 - 1629
    Cited by:  Papers (95)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (693 KB) | HTML iconHTML

    Pinning stabilization problem of linearly coupled stochastic neural networks (LCSNNs) is studied in this paper. A minimum number of controllers are used to force the LCSNNs to the desired equilibrium point by fully utilizing the structure of the network. In order to pinning control the LCSNNs to a certain desired state, only one controller is required for strongly connected network topology, and <... View full abstract»

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  • Adaptive Neural Control Design for Nonlinear Distributed Parameter Systems With Persistent Bounded Disturbances

    Publication Year: 2009, Page(s):1630 - 1644
    Cited by:  Papers (25)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (995 KB) | HTML iconHTML

    In this paper, an adaptive neural network (NN) control with a guaranteed L infin-gain performance is proposed for a class of parabolic partial differential equation (PDE) systems with unknown nonlinearities and persistent bounded disturbances. Initially, Galerkin method is applied to the PDE system to derive a low-order ordinary differential equation (ODE) system that accurately ... View full abstract»

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  • Universal Perceptron and DNA-Like Learning Algorithm for Binary Neural Networks: LSBF and PBF Implementations

    Publication Year: 2009, Page(s):1645 - 1658
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (986 KB) | HTML iconHTML

    Universal perceptron (UP), a generalization of Rosenblatt's perceptron, is considered in this paper, which is capable of implementing all Boolean functions (BFs). In the classification of BFs, there are: 1) linearly separable Boolean function (LSBF) class, 2) parity Boolean function (PBF) class, and 3) non-LSBF and non-PBF class. To implement these functions, UP takes different kinds of simple top... View full abstract»

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  • Pattern Classification With Class Probability Output Network

    Publication Year: 2009, Page(s):1659 - 1673
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (737 KB) | HTML iconHTML

    The output of a classifier is usually determined by the value of a discriminant function and a decision is made based on this output which does not necessarily represent the posterior probability for the soft decision of classification. In this context, it is desirable that the output of a classifier be calibrated in such a way to give the meaning of the posterior probability of class membership. ... View full abstract»

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  • Generalized Encoding and Decoding Operators for Lattice-Based Associative Memories

    Publication Year: 2009, Page(s):1674 - 1678
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (260 KB) | HTML iconHTML

    During the 1990s, Ritter introduced a new family of associative memories based on lattice algebra instead of linear algebra. These memories provide unlimited storage capacity, unlike linear-correlation-based models. The canonical lattice-based memories, however, are susceptible to noise in the initial input data. In this brief, we present novel methods of encoding and decoding lattice-based memori... View full abstract»

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  • Identifying the Topology of a Coupled FitzHugh–Nagumo Neurobiological Network via a Pinning Mechanism

    Publication Year: 2009, Page(s):1679 - 1684
    Cited by:  Papers (19)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (551 KB) | HTML iconHTML

    Topology identification of a network has received great interest for the reason that the study on many key properties of a network assumes a special known topology. Different from recent similar works in which the evolution of all the nodes in a complex network need to be received, this brief presents a novel criterion to identify the topology of a coupled FitzHugh-Nagumo (FHN) neurobiological net... View full abstract»

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  • 2010 IEEE World Congress on Computational Intelligence (WCCI)

    Publication Year: 2009, Page(s): 1685
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  • White box nonlinear prediction models

    Publication Year: 2009, Page(s): 1686
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  • Access over 1 million articles - The IEEE Digital Library [advertisement]

    Publication Year: 2009, Page(s): 1687
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  • Why we joined ... [advertisement]

    Publication Year: 2009, Page(s): 1688
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2009, Page(s): C3
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  • IEEE Transactions on Neural Networks Information for authors

    Publication Year: 2009, 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