IEEE Transactions on Neural Networks and Learning Systems

Issue 3 • March 2014

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  • Table of contents

    Publication Year: 2014, Page(s): C1
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  • IEEE Transactions on Neural Networks and Learning Systems - publication information

    Publication Year: 2014, Page(s): C2
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  • A Survey on CPG-Inspired Control Models and System Implementation

    Publication Year: 2014, Page(s):441 - 456
    Cited by:  Papers (44)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1998 KB) | HTML iconHTML

    This paper surveys the developments of the last 20 years in the field of central pattern generator (CPG) inspired locomotion control, with particular emphasis on the fast emerging robotics-related applications. Functioning as a biological neural network, CPGs can be considered as a group of coupled neurons that generate rhythmic signals without sensory feedback; however, sensory feedback is needed... View full abstract»

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  • Robust Model Predictive Control of Nonlinear Systems With Unmodeled Dynamics and Bounded Uncertainties Based on Neural Networks

    Publication Year: 2014, Page(s):457 - 469
    Cited by:  Papers (34)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (3013 KB) | HTML iconHTML

    This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal model is available. This partially known nonlinear model is first decomposed to an affine term plus an u... View full abstract»

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  • Multi-Level Fuzzy Min-Max Neural Network Classifier

    Publication Year: 2014, Page(s):470 - 482
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (2486 KB) | HTML iconHTML

    In this paper a multi-level fuzzy min-max neural network classifier (MLF), which is a supervised learning method, is described. MLF uses basic concepts of the fuzzy min-max (FMM) method in a multi-level structure to classify patterns. This method uses separate classifiers with smaller hyperboxes in different levels to classify the samples that are located in overlapping regions. The final output o... View full abstract»

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  • Adaptive Identifier for Uncertain Complex Nonlinear Systems Based on Continuous Neural Networks

    Publication Year: 2014, Page(s):483 - 494
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1320 KB) | HTML iconHTML

    This paper presents the design of a complex-valued differential neural network identifier for uncertain nonlinear systems defined in the complex domain. This design includes the construction of an adaptive algorithm to adjust the parameters included in the identifier. The algorithm is obtained based on a special class of controlled Lyapunov functions. The quality of the identification process is c... View full abstract»

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  • Function Approximation Using Combined Unsupervised and Supervised Learning

    Publication Year: 2014, Page(s):495 - 505
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1110 KB) | HTML iconHTML

    Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimen... View full abstract»

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  • Active Learning of Pareto Fronts

    Publication Year: 2014, Page(s):506 - 519
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1934 KB) | HTML iconHTML

    This paper introduces the active learning of Pareto fronts (ALP) algorithm, a novel approach to recover the Pareto front of a multiobjective optimization problem. ALP casts the identification of the Pareto front into a supervised machine learning task. This approach enables an analytical model of the Pareto front to be built. The computational effort in generating the supervised information is red... View full abstract»

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  • Learning Harmonium Models With Infinite Latent Features

    Publication Year: 2014, Page(s):520 - 532
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (2493 KB) | HTML iconHTML

    Undirected latent variable models represent an important class of graphical models that have been successfully developed to deal with various tasks. One common challenge in learning such models is to determine the number of hidden units that are unknown a priori. Although Bayesian nonparametrics have provided promising results in bypassing the model selection problem in learning directed Bayesian ... View full abstract»

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  • A Class of Quaternion Kalman Filters

    Publication Year: 2014, Page(s):533 - 544
    Cited by:  Papers (20)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (2055 KB) | HTML iconHTML

    The existing Kalman filters for quaternion-valued signals do not operate fully in the quaternion domain, and are combined with the real Kalman filter to enable the tracking in 3-D spaces. Using the recently introduced HR-calculus, we develop the fully quaternion-valued Kalman filter (QKF) and quaternion-extended Kalman filter (QEKF), allowing for the tracking of 3-D and 4-D signals directly in the... View full abstract»

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  • Neural Network for Nonsmooth, Nonconvex Constrained Minimization Via Smooth Approximation

    Publication Year: 2014, Page(s):545 - 556
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (837 KB) | HTML iconHTML

    A neural network based on smoothing approximation is presented for a class of nonsmooth, nonconvex constrained optimization problems, where the objective function is nonsmooth and nonconvex, the equality constraint functions are linear and the inequality constraint functions are nonsmooth, convex. This approach can find a Clarke stationary point of the optimization problem by following a continuou... View full abstract»

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  • Nonbinary Associative Memory With Exponential Pattern Retrieval Capacity and Iterative Learning

    Publication Year: 2014, Page(s):557 - 570
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1516 KB) | HTML iconHTML

    We consider the problem of neural association for a network of nonbinary neurons. Here, the task is to first memorize a set of patterns using a network of neurons whose states assume values from a finite number of integer levels. Later, the same network should be able to recall the previously memorized patterns from their noisy versions. Prior work in this area consider storing a finite number of ... View full abstract»

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  • A Constrained Backpropagation Approach for the Adaptive Solution of Partial Differential Equations

    Publication Year: 2014, Page(s):571 - 584
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (6303 KB) | HTML iconHTML

    This paper presents a constrained backpropagation (CPROP) methodology for solving nonlinear elliptic and parabolic partial differential equations (PDEs) adaptively, subject to changes in the PDE parameters or external forcing. Unlike existing methods based on penalty functions or Lagrange multipliers, CPROP solves the constrained optimization problem associated with training a neural network to ap... View full abstract»

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  • A Robust and Scalable Neuromorphic Communication System by Combining Synaptic Time Multiplexing and MIMO-OFDM

    Publication Year: 2014, Page(s):585 - 608
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    This paper describes a novel architecture for enabling robust and efficient neuromorphic communication. The architecture combines two concepts: 1) synaptic time multiplexing (STM) that trades space for speed of processing to create an intragroup communication approach that is firing rate independent and offers more flexibility in connectivity than cross-bar architectures and 2) a wired multiple in... View full abstract»

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  • ERNN: A Biologically Inspired Feedforward Neural Network to Discriminate Emotion From EEG Signal

    Publication Year: 2014, Page(s):609 - 620
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (2584 KB) | HTML iconHTML

    Emotions play an important role in human cognition, perception, decision making, and interaction. This paper presents a six-layer biologically inspired feedforward neural network to discriminate human emotions from EEG. The neural network comprises a shift register memory after spectral filtering for the input layer, and the estimation of coherence between each pair of input signals for the hidden... View full abstract»

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  • Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems

    Publication Year: 2014, Page(s):621 - 634
    Cited by:  Papers (130)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (2635 KB) | HTML iconHTML

    This paper is concerned with a new discrete-time policy iteration adaptive dynamic programming (ADP) method for solving the infinite horizon optimal control problem of nonlinear systems. The idea is to use an iterative ADP technique to obtain the iterative control law, which optimizes the iterative performance index function. The main contribution of this paper is to analyze the convergence and st... View full abstract»

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  • Reinforcement Learning Output Feedback NN Control Using Deterministic Learning Technique

    Publication Year: 2014, Page(s):635 - 641
    Cited by:  Papers (101)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (663 KB) | HTML iconHTML

    In this brief, a novel adaptive-critic-based neural network (NN) controller is investigated for nonlinear pure-feedback systems. The controller design is based on the transformed predictor form, and the actor-critic NN control architecture includes two NNs, whereas the critic NN is used to approximate the strategic utility function, and the action NN is employed to minimize both the strategic util... View full abstract»

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  • 2014 IEEE World Congress on Computational Intelligence

    Publication Year: 2014, Page(s): 642
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  • 2014 IEEE Symposium Series on Computational Intelligience

    Publication Year: 2014, Page(s): 643
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  • Open Access

    Publication Year: 2014, Page(s): 644
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2014, Page(s): C3
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  • IEEE Transactions on Neural Networks information for authors

    Publication Year: 2014, 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