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IEEE Transactions on Neural Networks and Learning Systems

Issue 3 • Date March 2013

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  • 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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  • Dissipativity Analysis for Discrete-Time Stochastic Neural Networks With Time-Varying Delays

    Publication Year: 2013, Page(s):345 - 355
    Cited by:  Papers (43)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (319 KB) | HTML iconHTML

    In this paper, the problem of dissipativity analysis is discussed for discrete-time stochastic neural networks with time-varying discrete and finite-distributed delays. The discretized Jensen inequality and lower bounds lemma are adopted to deal with the involved finite sum quadratic terms, and a sufficient condition is derived to ensure the considered neural networks to be globally asymptotically... View full abstract»

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  • Model-Based Online Learning With Kernels

    Publication Year: 2013, Page(s):356 - 369
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (905 KB) | HTML iconHTML

    New optimization models and algorithms for online learning with Kernels (OLK) in classification, regression, and novelty detection are proposed in a reproducing Kernel Hilbert space. Unlike the stochastic gradient descent algorithm, called the naive online Reg minimization algorithm (NORMA), OLK algorithms are obtained by solving a constrained optimization problem based on the proposed models. By ... View full abstract»

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  • Adaptive Control for Nonlinear Pure-Feedback Systems With High-Order Sliding Mode Observer

    Publication Year: 2013, Page(s):370 - 382
    Cited by:  Papers (36)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (907 KB) | HTML iconHTML

    Most of the available control schemes for pure-feedback systems are derived based on the backstepping technique. On the contrary, this paper presents a novel adaptive control design for nonlinear pure-feedback systems without using backstepping. By introducing a set of alternative state variables and the corresponding transform, state-feedback control of the pure-feedback system can be viewed as o... View full abstract»

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  • Low-Rank Structure Learning via Nonconvex Heuristic Recovery

    Publication Year: 2013, Page(s):383 - 396
    Cited by:  Papers (23)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (719 KB) | HTML iconHTML

    In this paper, we propose a nonconvex framework to learn the essential low-rank structure from corrupted data. Different from traditional approaches, which directly utilizes convex norms to measure the sparseness, our method introduces more reasonable nonconvex measurements to enhance the sparsity in both the intrinsic low-rank structure and the sparse corruptions. We will, respectively, introduce... View full abstract»

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  • Synaptic Variability in a Cortical Neuromorphic Circuit

    Publication Year: 2013, Page(s):397 - 409
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1556 KB) | HTML iconHTML

    Variable behavior has been observed in several mechanisms found in biological neurons, resulting in changes in neural behavior that might be useful to capture in neuromorphic circuits. This paper presents a neuromorphic cortical neuron with synaptic neurotransmitter-release variability, which is designed to be used in neural networks as part of the Biomimetic Real-Time Cortex project. This neuron ... View full abstract»

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  • Sampled-Data Synchronization of Chaotic Lur'e Systems With Time Delays

    Publication Year: 2013, Page(s):410 - 421
    Cited by:  Papers (40)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1870 KB) | HTML iconHTML

    This paper studies the problem of sampled-data control for master-slave synchronization schemes that consist of identical chaotic Lur'e systems with time delays. It is assumed that the sampling periods are arbitrarily varying but bounded. In order to take full advantage of the available information about the actual sampling pattern, a novel Lyapunov functional is proposed, which is positive defini... View full abstract»

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  • Multiplicative Update Rules for Concurrent Nonnegative Matrix Factorization and Maximum Margin Classification

    Publication Year: 2013, Page(s):422 - 434
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (565 KB) | HTML iconHTML

    The state-of-the-art classification methods which employ nonnegative matrix factorization (NMF) employ two consecutive independent steps. The first one performs data transformation (dimensionality reduction) and the second one classifies the transformed data using classification methods, such as nearest neighbor/centroid or support vector machines (SVMs). In the following, we focus on using NMF fa... View full abstract»

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  • Distributed Synchronization of Coupled Neural Networks via Randomly Occurring Control

    Publication Year: 2013, Page(s):435 - 447
    Cited by:  Papers (103)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3019 KB) | HTML iconHTML

    In this paper, we study the distributed synchronization and pinning distributed synchronization of stochastic coupled neural networks via randomly occurring control. Two Bernoulli stochastic variables are used to describe the occurrences of distributed adaptive control and updating law according to certain probabilities. Both distributed adaptive control and updating law for each vertex in a netwo... View full abstract»

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  • Portfolio of Automated Trading Systems: Complexity and Learning Set Size Issues

    Publication Year: 2013, Page(s):448 - 459
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (595 KB) | HTML iconHTML

    In this paper, we consider using profit/loss histories of multiple automated trading systems (ATSs) as N input variables in portfolio management. By means of multivariate statistical analysis and simulation studies, we analyze the influences of sample size (L) and input dimensionality on the accuracy of determining the portfolio weights. We find that degradation in portfolio performance due to ine... View full abstract»

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  • Regularized Mixture Density Estimation With an Analytical Setting of Shrinkage Intensities

    Publication Year: 2013, Page(s):460 - 470
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (781 KB) | HTML iconHTML

    In this paper, we propose a method for P-variate probability density estimation assuming a Gaussian mixture model (GMM). Our method exploits a regularization technique for improving the estimation accuracy of the GMM component covariance matrices. We derive an expectation maximization algorithm for fitting our regularized GMM (RGMM), which exploits an analytical Ledoit-Wolf-type shrinkage estimati... View full abstract»

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  • Stochastic Optimal Controller Design for Uncertain Nonlinear Networked Control System via Neuro Dynamic Programming

    Publication Year: 2013, Page(s):471 - 484
    Cited by:  Papers (37)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (825 KB) | HTML iconHTML

    The stochastic optimal controller design for the nonlinear networked control system (NNCS) with uncertain system dynamics is a challenging problem due to the presence of both system nonlinearities and communication network imperfections, such as random delays and packet losses, which are not unknown a priori. In the recent literature, neuro dynamic programming (NDP) techniques, based on value and ... View full abstract»

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  • Simple Exponential Family PCA

    Publication Year: 2013, Page(s):485 - 497
    Cited by:  Papers (23)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (722 KB) | HTML iconHTML

    Principal component analysis (PCA) is a widely used model for dimensionality reduction. In this paper, we address the problem of determining the intrinsic dimensionality of a general type data population by selecting the number of principal components for a generalized PCA model. In particular, we propose a generalized Bayesian PCA model, which deals with general type data by employing exponential... View full abstract»

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  • Dynamics Analysis of a Population Decoding Model

    Publication Year: 2013, Page(s):498 - 503
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (354 KB) | HTML iconHTML

    Information processing in the nervous system involves the activity of large populations of neurons. It is difficult to extract information from these population codes because of the noise inherent in neuronal responses. We propose a divisive normalization model to read the population codes. The dynamics of the model are analyzed by continuous attractor theory. Under certain conditions, the model p... View full abstract»

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  • Learning With Kernel Smoothing Models and Low-Discrepancy Sampling

    Publication Year: 2013, Page(s):504 - 509
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (275 KB) | HTML iconHTML

    This brief presents an analysis of the performance of kernel smoothing models used to estimate an unknown target function, addressing the case where the choice of the training set is part of the learning process. In particular, we consider a choice of the points at which the function is observed based on low-discrepancy sequences, which is a family of sampling methods commonly employed for efficie... View full abstract»

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  • 2014 IEEE World Congress on Computational Intelligence

    Publication Year: 2013, Page(s): 510
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  • Open Access

    Publication Year: 2013, Page(s): 511
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  • IEEE Xplore Digital Library

    Publication Year: 2013, Page(s): 512
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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