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IEEE Transactions on Neural Networks and Learning Systems

Issue 5 • Date May 2013

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Displaying Results 1 - 19 of 19
  • 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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  • Nonstationary Source Separation Using Sequential and Variational Bayesian Learning

    Publication Year: 2013, Page(s):681 - 694
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (760 KB) | HTML iconHTML

    Independent component analysis (ICA) is a popular approach for blind source separation where the mixing process is assumed to be unchanged with a fixed set of stationary source signals. However, the mixing system and source signals are nonstationary in real-world applications, e.g., the source signals may abruptly appear or disappear, the sources may be replaced by new ones or even moving by time.... View full abstract»

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  • Complex-Valued Filtering Based on the Minimization of Complex-Error Entropy

    Publication Year: 2013, Page(s):695 - 708
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (844 KB) | HTML iconHTML

    In this paper, we consider the training of complex-valued filter based on the information theoretic method. We first generalize the error entropy criterion to complex domain to present the complex error entropy criterion (CEEC). Due to the difficulty in estimating the entropy of complex-valued error directly, the entropy bound minimization (EBM) method is used to compute the upper bounds of the en... View full abstract»

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  • Multiview Vector-Valued Manifold Regularization for Multilabel Image Classification

    Publication Year: 2013, Page(s):709 - 722
    Cited by:  Papers (48)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2402 KB) | HTML iconHTML

    In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g., pedestrian, bicycle, and tree) and is properly characterized by multiple visual features (e.g., color, texture, and shape). Currently, available tools ignore either the label relationship or the view compleme... View full abstract»

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  • Stopped Object Detection by Learning Foreground Model in Videos

    Publication Year: 2013, Page(s):723 - 735
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (899 KB) | HTML iconHTML

    The automatic detection of objects that are abandoned or removed in a video scene is an interesting area of computer vision, with key applications in video surveillance. Forgotten or stolen luggage in train and airport stations and irregularly parked vehicles are examples that concern significant issues, such as the fight against terrorism and crime, and public safety. Both issues involve the basi... View full abstract»

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  • Asynchronous Cellular Automaton-Based Neuron: Theoretical Analysis and On-FPGA Learning

    Publication Year: 2013, Page(s):736 - 748
    Cited by:  Papers (14)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3592 KB) | HTML iconHTML

    A generalized asynchronous cellular automaton-based neuron model is a special kind of cellular automaton that is designed to mimic the nonlinear dynamics of neurons. The model can be implemented as an asynchronous sequential logic circuit and its control parameter is the pattern of wires among the circuit elements that is adjustable after implementation in a field-programmable gate array (FPGA) de... View full abstract»

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  • Soft Margin Multiple Kernel Learning

    Publication Year: 2013, Page(s):749 - 761
    Cited by:  Papers (26)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (843 KB) | HTML iconHTML

    Multiple kernel learning (MKL) has been proposed for kernel methods by learning the optimal kernel from a set of predefined base kernels. However, the traditional L1MKL method often achieves worse results than the simplest method using the average of base kernels (i.e., average kernel) in some practical applications. In order to improve the effectiveness of MKL, this paper presen... View full abstract»

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  • Online Learning Control Using Adaptive Critic Designs With Sparse Kernel Machines

    Publication Year: 2013, Page(s):762 - 775
    Cited by:  Papers (40)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (632 KB) | HTML iconHTML

    In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization c... View full abstract»

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  • Policy Improvement by a Model-Free Dyna Architecture

    Publication Year: 2013, Page(s):776 - 788
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (810 KB) | HTML iconHTML

    The objective of this paper is to accelerate the process of policy improvement in reinforcement learning. The proposed Dyna-style system combines two learning schemes, one of which utilizes a temporal difference method for direct learning; the other uses relative values for indirect learning in planning between two successive direct learning cycles. Instead of establishing a complicated world mode... View full abstract»

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  • Firing Rate Propagation Through Neuronal–Astrocytic Network

    Publication Year: 2013, Page(s):789 - 799
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (5206 KB) | HTML iconHTML

    Understanding the underlying mechanism of the propagation of neuronal activities within the brain is a fundamental issue in neuroscience. Traditionally, communication and information processing have been exclusively considered as the province of synaptic coupling between neurons. Astrocytes, however, have recently been acknowledged as active partners in neuronal information processing. So, it is m... View full abstract»

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  • On Stabilization of Stochastic Cohen-Grossberg Neural Networks With Mode-Dependent Mixed Time-Delays and Markovian Switching

    Publication Year: 2013, Page(s):800 - 811
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (494 KB) | HTML iconHTML

    The globally exponential stabilization problem is investigated for a general class of stochastic Cohen-Grossberg neural networks with both Markovian jumping parameters and mixed mode-dependent time-delays. The mixed time-delays consist of both discrete and distributed delays. This paper aims to design a memoryless state feedback controller such that the closed-loop system is stochastically exponen... View full abstract»

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  • A One-Layer Projection Neural Network for Nonsmooth Optimization Subject to Linear Equalities and Bound Constraints

    Publication Year: 2013, Page(s):812 - 824
    Cited by:  Papers (40)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2958 KB) | HTML iconHTML

    This paper presents a one-layer projection neural network for solving nonsmooth optimization problems with generalized convex objective functions and subject to linear equalities and bound constraints. The proposed neural network is designed based on two projection operators: linear equality constraints, and bound constraints. The objective function in the optimization problem can be any nonsmooth... View full abstract»

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  • Constraint Verification With Kernel Machines

    Publication Year: 2013, Page(s):825 - 831
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (740 KB) | HTML iconHTML

    Based on a recently proposed framework of learning from constraints using kernel-based representations, in this brief, we naturally extend its application to the case of inferences on new constraints. We give examples for polynomials and first-order logic by showing how new constraints can be checked on the basis of given premises and data samples. Interestingly, this gives rise to a perceptual lo... View full abstract»

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  • Energy-Efficient SVM Learning Control System for Biped Walking Robots

    Publication Year: 2013, Page(s):831 - 837
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (442 KB) | HTML iconHTML

    An energy-efficient support vector machine (EE-SVM) learning control system considering the energy cost of each training sample of biped dynamic is proposed to realize energy-efficient biped walking. Energy costs of the biped walking samples are calculated. Then the samples are weighed with the inverses of the energy costs. An EE-SVM objective function with energy-related slack variables is propos... View full abstract»

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  • Self-Tuning Control With a Filter and a Neural Compensator for a Class of Nonlinear Systems

    Publication Year: 2013, Page(s):837 - 843
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (311 KB) | HTML iconHTML

    Considering the mismatching of model-process order, in this brief, a self-tuning proportional-integral-derivative (PID)-like controller is proposed by combining a pole assignment self-tuning PID controller with a filter and a neural compensator. To design the PID controller, a reduced order model is introduced, whose linear parameters are identified by a normalized projection algorithm with a dead... View full abstract»

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  • 2014 IEEE World Congress on Computational Intelligence

    Publication Year: 2013, Page(s): 844
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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