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IEEE Transactions on Neural Networks

Issue 3 • March 2008

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Displaying Results 1 - 24 of 24
  • Table of contents

    Publication Year: 2008, Page(s):C1 - C4
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  • IEEE Transactions on Neural Networks publication information

    Publication Year: 2008, Page(s): C2
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  • Selecting Useful Groups of Features in a Connectionist Framework

    Publication Year: 2008, Page(s):381 - 396
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (688 KB) | HTML iconHTML

    Suppose for a given classification or function approximation (FA) problem data are collected using sensors. From the output of the th sensor, features are extracted, thereby generating features, so for the task we have as input data along with their corresponding outputs or class labels . Here, we propose two connectionist schemes that can simultaneously select the useful sensors and learn the rel... View full abstract»

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  • Global Exponential Stability of Bidirectional Associative Memory Neural Networks With Time Delays

    Publication Year: 2008, Page(s):397 - 407
    Cited by:  Papers (23)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (607 KB) | HTML iconHTML

    In this paper, we consider delayed bidirectional associative memory (BAM) neural networks (NNs) with Lipschitz continuous activation functions. By applying Young's inequality and Holder's inequality techniques together with the properties of monotonic continuous functions, global exponential stability criteria are established for BAM NNs with time delays. This is done through the use of a new Lyap... View full abstract»

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  • A Class of Complex ICA Algorithms Based on the Kurtosis Cost Function

    Publication Year: 2008, Page(s):408 - 420
    Cited by:  Papers (42)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (769 KB) | HTML iconHTML

    In this paper, we introduce a novel way of performing real-valued optimization in the complex domain. This framework enables a direct complex optimization technique when the cost function satisfies the Brandwood's independent analyticity condition. In particular, this technique has been used to derive three algorithms, namely, kurtosis maximization using gradient update (KM-G), kurtosis maximizati... View full abstract»

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  • A Hybrid Technique for Blind Separation of Non-Gaussian and Time-Correlated Sources Using a Multicomponent Approach

    Publication Year: 2008, Page(s):421 - 430
    Cited by:  Papers (26)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1269 KB) | HTML iconHTML

    Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient fastICA (EFICA) offers an asymptotically optimal solution to this problem when all of the sources obey a generalized Gaussian distribution, at most one of them is Gaussian, and each is independent and identically distributed (i.i.d.) in time. Like... View full abstract»

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  • Performing Feature Selection With Multilayer Perceptrons

    Publication Year: 2008, Page(s):431 - 441
    Cited by:  Papers (33)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (541 KB) | HTML iconHTML

    An experimental study on two decision issues for wrapper feature selection (FS) with multilayer perceptrons and the sequential backward selection (SBS) procedure is presented. The decision issues studied are the stopping criterion and the network retraining before computing the saliency. Experimental results indicate that the increase in the computational cost associated with retraining the networ... View full abstract»

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  • Automatic Cluster Detection in Kohonen's SOM

    Publication Year: 2008, Page(s):442 - 459
    Cited by:  Papers (37)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3430 KB) | HTML iconHTML

    Kohonen's self-organizing map (SOM) is a popular neural network architecture for solving problems in the field of explorative data analysis, clustering, and data visualization. One of the major drawbacks of the SOM algorithm is the difficulty for nonexpert users to interpret the information contained in a trained SOM. In this paper, this problem is addressed by introducing an enhanced version of t... View full abstract»

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  • Self-Organizing Radial Basis Function Network for Real-Time Approximation of Continuous-Time Dynamical Systems

    Publication Year: 2008, Page(s):460 - 474
    Cited by:  Papers (37)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1586 KB) | HTML iconHTML

    Real-time approximators for continuous-time dynamical systems with many inputs are presented. These approximators employ a novel self-organizing radial basis function (RBF) network, which varies its structure dynamically to keep the prescribed approximation accuracy. The RBFs can be added or removed online in order to achieve the appropriate network complexity for the real-time approximation of th... View full abstract»

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  • Two-Microphone Separation of Speech Mixtures

    Publication Year: 2008, Page(s):475 - 492
    Cited by:  Papers (35)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2798 KB) | HTML iconHTML

    Separation of speech mixtures, often referred to as the cocktail party problem, has been studied for decades. In many source separation tasks, the separation method is limited by the assumption of at least as many sensors as sources. Further, many methods require that the number of signals within the recorded mixtures be known in advance. In many real-world applications, these limitations are too ... View full abstract»

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  • A Fault-Tolerant Regularizer for RBF Networks

    Publication Year: 2008, Page(s):493 - 507
    Cited by:  Papers (25)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (776 KB) | HTML iconHTML

    In classical training methods for node open fault, we need to consider many potential faulty networks. When the multinode fault situation is considered, the space of potential faulty networks is very large. Hence, the objective function and the corresponding learning algorithm would be computationally complicated. This paper uses the Kullback-Leibler divergence to define an objective function for ... View full abstract»

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  • An Expectation–Maximization Method for Spatio–Temporal Blind Source Separation Using an AR-MOG Source Model

    Publication Year: 2008, Page(s):508 - 519
    Cited by:  Papers (19)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (525 KB) | HTML iconHTML

    In this paper, we develop a maximum-likelihood (ML) spatio-temporal blind source separation (BSS) algorithm, where the temporal dependencies are explained by assuming that each source is an autoregressive (AR) process and the distribution of the associated independent identically distributed (i.i.d.) innovations process is described using a mixture of Gaussians. Unlike most ML methods, the propose... View full abstract»

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  • Exponential Stability of Discrete-Time Genetic Regulatory Networks With Delays

    Publication Year: 2008, Page(s):520 - 523
    Cited by:  Papers (93)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (301 KB) | HTML iconHTML

    Discrete-time versions of the continuous-time genetic regulatory networks (GRNs) with SUM regulatory functions are formulated and studied in this letter. Sufficient conditions are derived to ensure the global exponential stability of the discrete-time GRNs with delays. An illustrative example is given to demonstrate the effectiveness of the obtained results. View full abstract»

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  • Wavelet Basis Function Neural Networks for Sequential Learning

    Publication Year: 2008, Page(s):523 - 528
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (405 KB) | HTML iconHTML

    In this letter, we develop the wavelet basis function neural networks (WBFNNs). It is analogous to radial basis function neural networks (RBFNNs) and to wavelet neural networks (WNNs). In WBFNNs, both the scaling function and the wavelet function of a multiresolution approximation (MRA) are adopted as the basis for approximating functions. A sequential learning algorithm for WBFNNs is presented an... View full abstract»

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  • An Improved Algebraic Criterion for Global Exponential Stability of Recurrent Neural Networks With Time-Varying Delays

    Publication Year: 2008, Page(s):528 - 531
    Cited by:  Papers (74)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (277 KB) | HTML iconHTML

    This brief paper presents an M-matrix-based algebraic criterion for the global exponential stability of a class of recurrent neural networks with decreasing time-varying delays. The criterion improves some previous criteria based on M-matrix and is easy to be verified with the connection weights of the recurrent neural networks with decreasing time-varying delays. In addition, the rate of exponent... View full abstract»

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  • A New Criterion of Delay-Dependent Asymptotic Stability for Hopfield Neural Networks With Time Delay

    Publication Year: 2008, Page(s):532 - 535
    Cited by:  Papers (140)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (157 KB) | HTML iconHTML

    In this brief, the problem of global asymptotic stability for delayed Hopfield neural networks (HNNs) is investigated. A new criterion of asymptotic stability is derived by introducing a new kind of Lyapunov-Krasovskii functional and is formulated in terms of a linear matrix inequality (LMI), which can be readily solved via standard software. This new criterion based on a delay fractioning approac... View full abstract»

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  • Energy Function and Energy Evolution on Neuronal Populations

    Publication Year: 2008, Page(s):535 - 538
    Cited by:  Papers (19)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (140 KB) | HTML iconHTML

    Based on the principle of energy coding, an energy function of a variety of electric potentials of a neural population in cerebral cortex is formulated. The energy function is used to describe the energy evolution of the neuronal population with time and the coupled relationship between neurons at the subthreshold and the suprathreshold states. The Hamiltonian motion equation with the membrane pot... View full abstract»

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  • On a Neural Approximator to ODEs

    Publication Year: 2008, Page(s):539 - 543
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (200 KB) | HTML iconHTML

    The object of this brief is to present and analyze the training of a single-layer neural network in order to solve ordinary differential equations (ODEs). Properties of the approximator are derived and some examples of its application are shown. View full abstract»

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  • Complex-Valued Neural Networks (Hirose, A.; 2006) [Book review]

    Publication Year: 2008, Page(s): 544
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  • International Workshop on Computational Intelligence in Security for Information Systems

    Publication Year: 2008, Page(s): 545
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  • 2008 IEEE World Congress on Computational Intelligence

    Publication Year: 2008, Page(s): 546
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  • Explore IEL IEEE's most comprehensive resource [advertisement]

    Publication Year: 2008, Page(s): 547
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  • IEEE Foundation [advertisement]

    Publication Year: 2008, Page(s): 548
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2008, Page(s): C3
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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