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IEEE Transactions on Neural Networks

Issue 9 • Date Sept. 2011

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  • Table of contents

    Publication Year: 2011, Page(s): C1
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  • IEEE Transactions on Neural Networks publication information

    Publication Year: 2011, Page(s): C2
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  • Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances

    Publication Year: 2011, Page(s):1341 - 1356
    Cited by:  Papers (28)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (773 KB) | HTML iconHTML

    This paper evaluates the four leading techniques proposed in the literature for construction of prediction intervals (PIs) for neural network point forecasts. The delta, Bayesian, bootstrap, and mean-variance estimation (MVE) methods are reviewed and their performance for generating high-quality PIs is compared. PI-based measures are proposed and applied for the objective and quantitative assessme... View full abstract»

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  • New Accurate and Flexible Design Procedure for a Stable KWTA Continuous Time Network

    Publication Year: 2011, Page(s):1357 - 1367
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (343 KB) | HTML iconHTML

    The classical continuous time recurrent (Hopfield) network is considered and adapted to K -winner-take-all operation. The neurons are of sigmoidal type with a controllable gain G, an amplitude m and interconnected by the conductance p. The network is intended to process one by one a sequence of lists, each of them with N distinct elements, each of them squeezed t... View full abstract»

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  • SortNet: Learning to Rank by a Neural Preference Function

    Publication Year: 2011, Page(s):1368 - 1380
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (578 KB) | HTML iconHTML

    Relevance ranking consists in sorting a set of objects with respect to a given criterion. However, in personalized retrieval systems, the relevance criteria may usually vary among different users and may not be predefined. In this case, ranking algorithms that adapt their behavior from users' feedbacks must be devised. Two main approaches are proposed in the literature for learning to rank: the us... View full abstract»

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  • Online Identification of Nonlinear Spatiotemporal Systems Using Kernel Learning Approach

    Publication Year: 2011, Page(s):1381 - 1394
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1887 KB) | HTML iconHTML

    The identification of nonlinear spatiotemporal systems is of significance to engineering practice, since it can always provide useful insight into the underlying nonlinear mechanism and physical characteristics under study. In this paper, nonlinear spatiotemporal system models are transformed into a class of multi-input-multi-output (MIMO) partially linear systems (PLSs), and an effective online i... View full abstract»

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  • Discriminative Graph Embedding for Label Propagation

    Publication Year: 2011, Page(s):1395 - 1405
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (455 KB) | HTML iconHTML

    In many applications, the available information is encoded in graph structures. This is a common problem in biological networks, social networks, web communities and document citations. We investigate the problem of classifying nodes' labels on a similarity graph given only a graph structure on the nodes. Conventional machine learning methods usually require data to reside in some Euclidean spaces... View full abstract»

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  • Nonlinear Identification With Local Model Networks Using GTLS Techniques and Equality Constraints

    Publication Year: 2011, Page(s):1406 - 1418
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (718 KB) | HTML iconHTML

    Local model networks approximate a nonlinear system through multiple local models fitted within a partition space. The main advantage of this approach is that the identification of complex nonlinear processes is alleviated by the integration of structured knowledge about the process. This paper extends these concepts by the integration of quantitative process knowledge into the identification proc... View full abstract»

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  • Source Separation and Clustering of Phase-Locked Subspaces

    Publication Year: 2011, Page(s):1419 - 1434
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1522 KB) | HTML iconHTML

    It has been proven that there are synchrony (or phase-locking) phenomena present in multiple oscillating systems such as electrical circuits, lasers, chemical reactions, and human neurons. If the measurements of these systems cannot detect the individual oscillators but rather a superposition of them, as in brain electrophysiological signals (electo- and magneoencephalogram), spurious phase lockin... View full abstract»

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  • Echo State Gaussian Process

    Publication Year: 2011, Page(s):1435 - 1445
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (964 KB) | HTML iconHTML

    Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of benchmark tasks. In this paper, we introduce a ... View full abstract»

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  • Unsupervised Large Margin Discriminative Projection

    Publication Year: 2011, Page(s):1446 - 1456
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (770 KB) | HTML iconHTML

    We propose a new dimensionality reduction method called maximum margin projection (MMP), which aims to project data samples into the most discriminative subspace, where clusters are most well-separated. Specifically, MMP projects input patterns onto the normal of the maximum margin separating hyperplanes. As a result, MMP only depends on the geometry of the optimal decision boundary and not on the... View full abstract»

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  • A Dynamic Feedforward Neural Network Based on Gaussian Particle Swarm Optimization and its Application for Predictive Control

    Publication Year: 2011, Page(s):1457 - 1468
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (761 KB) | HTML iconHTML

    A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian fu... View full abstract»

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  • Parallel Reservoir Computing Using Optical Amplifiers

    Publication Year: 2011, Page(s):1469 - 1481
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (914 KB) | HTML iconHTML

    Reservoir computing (RC), a computational paradigm inspired on neural systems, has become increasingly popular in recent years for solving a variety of complex recognition and classification problems. Thus far, most implementations have been software-based, limiting their speed and power efficiency. Integrated photonics offers the potential for a fast, power efficient and massively parallel hardwa... View full abstract»

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  • Wide-Dynamic-Range APS-Based Silicon Retina With Brightness Constancy

    Publication Year: 2011, Page(s):1482 - 1493
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1112 KB) | HTML iconHTML

    A silicon retina is an intelligent vision sensor that can execute real-time image preprocessing by using a parallel analog circuit that mimics the structure of the neuronal circuits in the vertebrate retina. For enhancing the sensor's robustness to changes in illumination in a practical environment, we have designed and fabricated a silicon retina on the basis of a computational model of brightnes... View full abstract»

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  • Low-Complexity Nonlinear Adaptive Filter Based on a Pipelined Bilinear Recurrent Neural Network

    Publication Year: 2011, Page(s):1494 - 1507
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (836 KB) | HTML iconHTML

    To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each... View full abstract»

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  • Generalized Halanay Inequalities and Their Applications to Neural Networks With Unbounded Time-Varying Delays

    Publication Year: 2011, Page(s):1508 - 1513
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (211 KB) | HTML iconHTML

    In this brief, we discuss some variants of generalized Halanay inequalities that are useful in the discussion of dissipativity and stability of delayed neural networks, integro-differential systems, and Volterra functional differential equations. We provide some generalizations of the Halanay inequality, which is more accurate than the existing results. As applications, we discuss invariant set, d... View full abstract»

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  • Announcement - TNN will have a new name

    Publication Year: 2011, Page(s): 1514
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  • IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

    Publication Year: 2011, Page(s): 1515
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  • Introducing ieee.tv [advertisement]

    Publication Year: 2011, Page(s): 1516
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2011, Page(s): C3
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  • IEEE Transactions on Neural Networks Information for authors

    Publication Year: 2011, 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