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Neural Networks, IEEE Transactions on

Issue 11 • Date Nov. 2009

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  • 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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  • Bounded Influence Support Vector Regression for Robust Single-Model Estimation

    Publication Year: 2009 , Page(s): 1689 - 1706
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (2302 KB) |  | HTML iconHTML  

    Support vector regression (SVR) is now a well-established method for estimating real-valued functions. However, the standard SVR is not effective to deal with severe outlier contamination of both response and predictor variables commonly encountered in numerous real applications. In this paper, we present a bounded influence SVR, which downweights the influence of outliers in all the regression va... View full abstract»

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  • A New Neuroadaptive Control Architecture for Nonlinear Uncertain Dynamical Systems: Beyond \sigma - and e -Modifications

    Publication Year: 2009 , Page(s): 1707 - 1723
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (686 KB) |  | HTML iconHTML  

    This paper develops a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture involving additional terms in the update laws that are constructed using a moving time window of the integrated system uncertainty. These terms can be used to identify the ideal system weights of the neural network as well as effect... View full abstract»

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  • Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model

    Publication Year: 2009 , Page(s): 1724 - 1739
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1824 KB) |  | HTML iconHTML  

    This paper presents a modified pulse-coupled neural network (MPCNN) model for real-time collision-free path planning of mobile robots in nonstationary environments. The proposed neural network for robots is topologically organized with only local lateral connections among neurons. It works in dynamic environments and requires no prior knowledge of target or barrier movements. The target neuron fir... View full abstract»

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  • R-POPTVR: A Novel Reinforcement-Based POPTVR Fuzzy Neural Network for Pattern Classification

    Publication Year: 2009 , Page(s): 1740 - 1755
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (407 KB) |  | HTML iconHTML  

    In general, a fuzzy neural network (FNN) is characterized by its learning algorithm and its linguistic knowledge representation. However, it does not necessarily interact with its environment when the training data is assumed to be an accurate description of the environment under consideration. In interactive problems, it would be more appropriate for an agent to learn from its own experience thro... View full abstract»

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  • Learning Gaussian Mixture Models With Entropy-Based Criteria

    Publication Year: 2009 , Page(s): 1756 - 1771
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (2087 KB) |  | HTML iconHTML  

    In this paper, we address the problem of estimating the parameters of Gaussian mixture models. Although the expectation-maximization (EM) algorithm yields the maximum-likelihood (ML) solution, its sensitivity to the selection of the starting parameters is well-known and it may converge to the boundary of the parameter space. Furthermore, the resulting mixture depends on the number of selected comp... View full abstract»

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  • Disparity Estimation by Pooling Evidence From Energy Neurons

    Publication Year: 2009 , Page(s): 1772 - 1782
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (763 KB) |  | HTML iconHTML  

    In this paper, we propose an algorithm for disparity estimation from disparity energy neurons that seeks to maintain simplicity and biological plausibility, while also being based upon a formulation that enables us to interpret the model outputs probabilistically. We use the Bayes factor from statistical hypothesis testing to show that, in contradiction to the implicit assumption of many previousl... View full abstract»

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  • Variational Bayesian Mixture Model on a Subspace of Exponential Family Distributions

    Publication Year: 2009 , Page(s): 1783 - 1796
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1051 KB) |  | HTML iconHTML  

    Exponential principal component analysis (e-PCA) has been proposed to reduce the dimension of the parameters of probability distributions using Kullback information as a distance between two distributions. It also provides a framework for dealing with various data types such as binary and integer for which the Gaussian assumption on the data distribution is inappropriate. In this paper, we introdu... View full abstract»

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  • Adachi-Like Chaotic Neural Networks Requiring Linear-Time Computations by Enforcing a Tree-Shaped Topology

    Publication Year: 2009 , Page(s): 1797 - 1809
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (786 KB) |  | HTML iconHTML  

    The Adachi neural network (AdNN) is a fascinating neural network (NN) which has been shown to possess chaotic properties, and to also demonstrate associative memory (AM) and pattern recognition (PR) characteristics. Variants of the AdNN have also been used to obtain other PR phenomena, and even blurring. An unsurmountable problem associated with the AdNN and the variants referred to above is that ... View full abstract»

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  • Nonorthogonal Approximate Joint Diagonalization With Well-Conditioned Diagonalizers

    Publication Year: 2009 , Page(s): 1810 - 1819
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (579 KB) |  | HTML iconHTML  

    To make the results reasonable, existing joint diagonalization algorithms have imposed a variety of constraints on diagonalizers. Actually, those constraints can be imposed uniformly by minimizing the condition number of diagonalizers. Motivated by this, the approximate joint diagonalization problem is reviewed as a multiobjective optimization problem for the first time. Based on this, a new algor... View full abstract»

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  • Uncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning

    Publication Year: 2009 , Page(s): 1820 - 1836
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1873 KB) |  | HTML iconHTML  

    This paper proposes an uncorrelated multilinear principal component analysis (UMPCA) algorithm for unsupervised subspace learning of tensorial data. It should be viewed as a multilinear extension of the classical principal component analysis (PCA) framework. Through successive variance maximization, UMPCA seeks a tensor-to-vector projection (TVP) that captures most of the variation in the original... View full abstract»

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  • PSO-Based Cloning Template Design for CNN Associative Memories

    Publication Year: 2009 , Page(s): 1837 - 1841
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (199 KB) |  | HTML iconHTML  

    In this brief, a synthesis procedure for cellular neural networks (CNNs) with space-invariant cloning templates is proposed. The design algorithm is based on the use of the evolutionary algorithm of the particle swarm optimization (PSO) with the application to associative memories. The proposed synthesis procedure takes into account requirements in terms of robustness to parametric variations. Num... View full abstract»

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  • Nontrivial Global Attractors in 2-D Multistable Attractor Neural Networks

    Publication Year: 2009 , Page(s): 1842 - 1851
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1120 KB) |  | HTML iconHTML  

    Attractor dynamics is a crucial problem for attractor neural networks, as it is the underling computational mechanism for memory storage and retrieval in neural systems. This brief studies a class of attractor network consisting of linearized threshold neurons, and analyzes global attractors based on a parameterized 2-D model. On the basis of previous results on nondegenerate and degenerate equili... View full abstract»

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  • 2010 IEEE World Congress on Computational Intelligence (WCCI)

    Publication Year: 2009 , Page(s): 1852
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  • Special issue on White Box Nonlinear Prediction Models

    Publication Year: 2009 , Page(s): 1853
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  • Explore IEL IEEE's most comprehensive resource [advertisement]

    Publication Year: 2009 , Page(s): 1854
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  • IEEE copyright form

    Publication Year: 2009 , Page(s): 1855 - 1856
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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