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IEEE Transactions on Neural Networks

Issue 4 • Date April 2008

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  • 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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  • Delay-Dependent Criteria for Global Robust Periodicity of Uncertain Switched Recurrent Neural Networks With Time-Varying Delay

    Publication Year: 2008, Page(s):549 - 557
    Cited by:  Papers (28)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (479 KB) | HTML iconHTML

    In this paper, we introduce some ideas of switched systems into the field of neural networks and a large class of switched recurrent neural networks (SRNNs) with time-varying structured uncertainties and time-varying delay is investigated. Some delay-dependent robust periodicity criteria guaranteeing the existence, uniqueness, and global asymptotic stability of periodic solution for all admissible... View full abstract»

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  • A One-Layer Recurrent Neural Network With a Discontinuous Hard-Limiting Activation Function for Quadratic Programming

    Publication Year: 2008, Page(s):558 - 570
    Cited by:  Papers (90)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (842 KB) | HTML iconHTML

    In this paper, a one-layer recurrent neural network with a discontinuous hard-limiting activation function is proposed for quadratic programming. This neural network is capable of solving a large class of quadratic programming problems. The state variables of the neural network are proven to be globally stable and the output variables are proven to be convergent to optimal solutions as long as the... View full abstract»

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  • Locality-Preserved Maximum Information Projection

    Publication Year: 2008, Page(s):571 - 585
    Cited by:  Papers (47)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1681 KB) | HTML iconHTML

    Dimensionality reduction is usually involved in the domains of artificial intelligence and machine learning. Linear projection of features is of particular interest for dimensionality reduction since it is simple to calculate and analytically analyze. In this paper, we propose an essentially linear projection technique, called locality-preserved maximum information projection (LPMIP), to identify ... View full abstract»

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  • Shared Feature Extraction for Nearest Neighbor Face Recognition

    Publication Year: 2008, Page(s):586 - 595
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1116 KB) | HTML iconHTML

    In this paper, we propose a new supervised linear feature extraction technique for multiclass classification problems that is specially suited to the nearest neighbor classifier (NN). The problem of finding the optimal linear projection matrix is defined as a classification problem and the Adaboost algorithm is used to compute it in an iterative way. This strategy allows the introduction of a mult... View full abstract»

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  • Complex ICA by Negentropy Maximization

    Publication Year: 2008, Page(s):596 - 609
    Cited by:  Papers (63)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1133 KB) | HTML iconHTML

    In this paper, we use complex analytic functions to achieve independent component analysis (ICA) by maximization of non-Gaussianity and introduce the complex maximization of non-Gaussianity (CMN) algorithm. We derive both a gradient-descent and a quasi-Newton algorithm that use the full second-order statistics providing superior performance with circular and noncircular sources as compared to exis... View full abstract»

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  • Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines

    Publication Year: 2008, Page(s):610 - 624
    Cited by:  Papers (30)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1020 KB) | HTML iconHTML

    Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) was proposed that extends the large-margin idea to feature extraction. It often outperforms traditional methods such as kernel principal component analysis (KPCA) and kernel Fisher discriminant analysis (KFD). However, as in the S... View full abstract»

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  • DCT-Yager FNN: A Novel Yager-Based Fuzzy Neural Network With the Discrete Clustering Technique

    Publication Year: 2008, Page(s):625 - 644
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3021 KB) | HTML iconHTML

    Earlier clustering techniques such as the modified learning vector quantization (MLVQ) and the fuzzy Kohonen partitioning (FKP) techniques have focused on the derivation of a certain set of parameters so as to define the fuzzy sets in terms of an algebraic function. The fuzzy membership functions thus generated are uniform, normal, and convex. Since any irregular training data is clustered into un... View full abstract»

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  • Real-Time Reconfigurable Subthreshold CMOS Perceptron

    Publication Year: 2008, Page(s):645 - 657
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1082 KB) | HTML iconHTML

    In this paper, a new, real-time reconfigurable perceptron circuit element is presented. A six-transistor version used as a threshold gate, having a fan-in of three, producing adequate outputs for threshold of T = 1,2 and 3 is demonstrated by chip measurements. Subthreshold operation for supply voltages in the range of 100-350 mV is shown. The circuit performs competitively with a standard static c... View full abstract»

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  • Relevance-Based Feature Extraction for Hyperspectral Images

    Publication Year: 2008, Page(s):658 - 672
    Cited by:  Papers (28)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1136 KB) | HTML iconHTML

    Hyperspectral imagery affords researchers all discriminating details needed for fine delineation of many material classes. This delineation is essential for scientific research ranging from geologic to environmental impact studies. In a data mining scenario, one cannot blindly discard information because it can destroy discovery potential. In a supervised classification scenario, however, the pres... View full abstract»

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  • Output Feedback Stabilization for Time-Delay Nonlinear Interconnected Systems Using Neural Networks

    Publication Year: 2008, Page(s):673 - 688
    Cited by:  Papers (65)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (505 KB) | HTML iconHTML

    In this paper, dynamic output feedback control problem is investigated for a class of nonlinear interconnected systems with time delays. Decentralized observer independent of the time delays is first designed. Then, we employ the bounds information of uncertain interconnections to construct the decentralized output feedback controller via backstepping design method. Based on Lyapunov stability the... View full abstract»

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  • PSECMAC: A Novel Self-Organizing Multiresolution Associative Memory Architecture

    Publication Year: 2008, Page(s):689 - 712
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1446 KB) | HTML iconHTML

    The cerebellum constitutes a vital part of the human brain system that possesses the capability to model highly nonlinear physical dynamics. The cerebellar model articulation controller (CMAC) associative memory network is a computational model inspired by the neurophysiological properties of the cerebellum, and it has been widely used for control, optimization, and various pattern recognition tas... View full abstract»

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  • Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model

    Publication Year: 2008, Page(s):713 - 722
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (494 KB) | HTML iconHTML

    Previous work on statistical language modeling has shown that it is possible to train a feedforward neural network to approximate probabilities over sequences of words, resulting in significant error reduction when compared to standard baseline models based on n-grams. However, training the neural network model with the maximum-likelihood criterion requires computations proportional to the number ... View full abstract»

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  • A Note on the Bias in SVMs for Multiclassification

    Publication Year: 2008, Page(s):723 - 725
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (173 KB) | HTML iconHTML

    During the usual SVM biclassification learning process, the bias is chosen a posteriori as the value halfway between separating hyperplanes. A note on different approaches on the calculation of the bias when SVM is used for multiclassification is provided and empirical experimentation is carried out which shows that the accuracy rate can be improved by using bias formulations, although no single f... View full abstract»

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  • Further Results on Delay-Dependent Stability Criteria of Neural Networks With Time-Varying Delays

    Publication Year: 2008, Page(s):726 - 730
    Cited by:  Papers (68)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (199 KB) | HTML iconHTML

    In this brief paper, an augmented Lyapunov functional, which takes an integral term of state vector into account, is introduced. Owing to the functional, an improved delay-dependent asymptotic stability criterion for delayed neural networks (NNs) is derived in term of linear matrix inequalities (LMIs). It is shown that the obtained criterion can provide less conservative result than some existing ... View full abstract»

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  • Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches (Farrell, J.A. and Polycarpou, M.M. [Book review]

    Publication Year: 2008, Page(s):731 - 732
    Cited by:  Papers (2)
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  • 2008 IEEE World Congress on Computational Intelligence

    Publication Year: 2008, Page(s): 733
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  • Have you visited lately? www.ieee.org [advertisement]

    Publication Year: 2008, Page(s): 734
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  • Quality without compromise [advertisement]

    Publication Year: 2008, Page(s): 735
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  • Order form for reprints

    Publication Year: 2008, Page(s): 736
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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