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IEEE Transactions on Neural Networks and Learning Systems

Issue 8 • Date Aug. 2013

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Displaying Results 1 - 21 of 21
  • Table of contents

    Publication Year: 2013, Page(s): C1
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  • IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

    Publication Year: 2013, Page(s): C2
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  • Sampled-Data Exponential Synchronization of Complex Dynamical Networks With Time-Varying Coupling Delay

    Publication Year: 2013, Page(s):1177 - 1187
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (523 KB) | HTML iconHTML

    This paper studies the problem of sampled-data exponential synchronization of complex dynamical networks (CDNs) with time-varying coupling delay and uncertain sampling. By combining the time-dependent Lyapunov functional approach and convex combination technique, a criterion is derived to ensure the exponential stability of the error dynamics, which fully utilizes the available information about t... View full abstract»

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  • Dictionary Learning-Based Subspace Structure Identification in Spectral Clustering

    Publication Year: 2013, Page(s):1188 - 1199
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1415 KB) | HTML iconHTML

    In this paper, we study dictionary learning (DL) approach to identify the representation of low-dimensional subspaces from high-dimensional and nonnegative data. Such representation can be used to provide an affinity matrix among different subspaces for data clustering. The main contribution of this paper is to consider both nonnegativity and sparsity constraints together in DL such that data can ... View full abstract»

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  • Knowledge-Leverage-Based TSK Fuzzy System Modeling

    Publication Year: 2013, Page(s):1200 - 1212
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (764 KB) | HTML iconHTML

    Classical fuzzy system modeling methods consider only the current scene where the training data are assumed to be fully collectable. However, if the data available from the current scene are insufficient, the fuzzy systems trained by using the incomplete datasets will suffer from weak generalization capability for the prediction in the scene. In order to overcome this problem, a knowledge-leverage... View full abstract»

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  • A Cognitive Fault Diagnosis System for Distributed Sensor Networks

    Publication Year: 2013, Page(s):1213 - 1226
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2812 KB) | HTML iconHTML

    This paper introduces a novel cognitive fault diagnosis system (FDS) for distributed sensor networks that takes advantage of spatial and temporal relationships among sensors. The proposed FDS relies on a suitable functional graph representation of the network and a two-layer hierarchical architecture designed to promptly detect and isolate faults. The lower processing layer exploits a novel change... View full abstract»

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  • Boundedness and Complete Stability of Complex-Valued Neural Networks With Time Delay

    Publication Year: 2013, Page(s):1227 - 1238
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (782 KB) | HTML iconHTML

    In this paper, the boundedness and complete stability of complex-valued neural networks (CVNNs) with time delay are studied. Some conditions to guarantee the boundedness of the CVNNs are derived using local inhibition. Moreover, under the boundedness conditions, a compact set that globally attracts all the trajectories of the network is also given. Additionally, several conditions in terms of real... View full abstract»

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  • Fast Neuromimetic Object Recognition Using FPGA Outperforms GPU Implementations

    Publication Year: 2013, Page(s):1239 - 1252
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (870 KB) | HTML iconHTML

    Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired... View full abstract»

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  • Improving the Quality of Self-Organizing Maps by Self-Intersection Avoidance

    Publication Year: 2013, Page(s):1253 - 1265
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2036 KB) | HTML iconHTML

    The quality of self-organizing maps is always a key issue to practitioners. Smooth maps convey information about input data sets in a clear manner. Here a method is presented to modify the learning algorithm of self-organizing maps to reduce the number of topology errors, hence the obtained map has better quality at the expense of increased quantization error. It is based on avoiding maps that sel... View full abstract»

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  • Quantum-Based Algorithm for Optimizing Artificial Neural Networks

    Publication Year: 2013, Page(s):1266 - 1278
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (669 KB) | HTML iconHTML

    This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits... View full abstract»

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  • Hinging Hyperplanes for Time-Series Segmentation

    Publication Year: 2013, Page(s):1279 - 1291
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (809 KB) | HTML iconHTML

    Division of a time series into segments is a common technique for time-series processing, and is known as segmentation. Segmentation is traditionally done by linear interpolation in order to guarantee the continuity of the reconstructed time series. The interpolation-based segmentation methods may perform poorly for data with a level of noise because interpolation is noise sensitive. To handle the... View full abstract»

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  • Ranking Graph Embedding for Learning to Rerank

    Publication Year: 2013, Page(s):1292 - 1303
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (909 KB) | HTML iconHTML

    Dimensionality reduction is a key step to improving the generalization ability of reranking in image search. However, existing dimensionality reduction methods are typically designed for classification, clustering, and visualization, rather than for the task of learning to rank. Without using of ranking information such as relevance degree labels, direct utilization of conventional dimensionality ... View full abstract»

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  • Feasibility and Finite Convergence Analysis for Accurate On-Line \nu -Support Vector Machine

    Publication Year: 2013, Page(s):1304 - 1315
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (366 KB) | HTML iconHTML

    The ν-support vector machine ( ν-SVM) for classification has the advantage of using a parameter ν on controlling the number of support vectors and margin errors. Recently, an interesting accurate on-line algorithm accurate on-line ν-SVM algorithm (AONSVM) is proposed for training ν-SVM. AONSVM can be viewed as a special case of parametric quadratic programming te... View full abstract»

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  • Exponential Synchronization of Coupled Switched Neural Networks With Mode-Dependent Impulsive Effects

    Publication Year: 2013, Page(s):1316 - 1326
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (518 KB) | HTML iconHTML

    This paper investigates the synchronization problem of coupled switched neural networks (SNNs) with mode-dependent impulsive effects and time delays. The main feature of mode-dependent impulsive effects is that impulsive effects can exist not only at the instants coinciding with mode switching but also at the instants when there is no system switching. The impulses considered here include those th... View full abstract»

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  • Analysis of Boundedness and Convergence of Online Gradient Method for Two-Layer Feedforward Neural Networks

    Publication Year: 2013, Page(s):1327 - 1338
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (501 KB) | HTML iconHTML

    This paper presents a theoretical boundedness and convergence analysis of online gradient method for the training of two-layer feedforward neural networks. The well-known linear difference equation is extended to apply to the general case of linear or nonlinear activation functions. Based on this extended difference equation, we investigate the boundedness and convergence of the parameter sequence... View full abstract»

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  • Phase-Noise-Induced Resonance in Arrays of Coupled Excitable Neural Models

    Publication Year: 2013, Page(s):1339 - 1345
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1184 KB) | HTML iconHTML

    Recently, it is observed that, in a single neural model, phase noise (time-varying signal phase) arising from an external stimulating signal can induce regular spiking activities even if the signal is subthreshold. In addition, it is also uncovered that there exists an optimal phase noise intensity at which the spiking rhythm coincides with the frequency of the subthreshold signal, resulting in a ... View full abstract»

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  • 2014 IEEE World Congress on Computational Intelligence

    Publication Year: 2013, Page(s): 1346
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  • Two ways to Access the IEEE Member Digital Library

    Publication Year: 2013, Page(s): 1347
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  • IEEE Global History Network

    Publication Year: 2013, Page(s): 1348
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2013, Page(s): C3
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  • IEEE Transactions on Neural Networks information for authors

    Publication Year: 2013, Page(s): C4
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Aims & Scope

IEEE Transactions on Neural Networks and Learning Systems publishes technical articles that deal with the theory, design, and applications of neural networks and related learning systems.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Haibo He
Dept. of Electrical, Computer, and Biomedical Engineering
University of Rhode Island
Kingston, RI 02881, USA
ieeetnnls@gmail.com