By Topic

Neural Networks and Learning Systems, IEEE Transactions on

Issue 7 • Date July 2013

Filter Results

Displaying Results 1 - 22 of 22
  • Table of contents

    Publication Year: 2013 , Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (117 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Neural Networks and Learning Systems publication information

    Publication Year: 2013 , Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (138 KB)  
    Freely Available from IEEE
  • Enhancing Synchronizability of Diffusively Coupled Dynamical Networks: A Survey

    Publication Year: 2013 , Page(s): 1009 - 1022
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (443 KB) |  | HTML iconHTML  

    In this paper, we review the literature on enhancing synchronizability of diffusively coupled dynamical networks with identical nodes. The last decade has witnessed intensive investigations on the collective behavior over complex networks and synchronization of dynamical systems is the most common form of collective behavior. For many applications, it is desired that the synchronizability-the abil... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Sparse Representation Classifier Steered Discriminative Projection With Applications to Face Recognition

    Publication Year: 2013 , Page(s): 1023 - 1035
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (696 KB) |  | HTML iconHTML  

    A sparse representation-based classifier (SRC) is developed and shows great potential for real-world face recognition. This paper presents a dimensionality reduction method that fits SRC well. SRC adopts a class reconstruction residual-based decision rule, we use it as a criterion to steer the design of a feature extraction method. The method is thus called the SRC steered discriminative projectio... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Prediction Intervals for a Noisy Nonlinear Time Series Based on a Bootstrapping Reservoir Computing Network Ensemble

    Publication Year: 2013 , Page(s): 1036 - 1048
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (988 KB) |  | HTML iconHTML  

    Prediction intervals that provide estimated values as well as the corresponding reliability are applied to nonlinear time series forecast. However, constructing reliable prediction intervals for noisy time series is still a challenge. In this paper, a bootstrapping reservoir computing network ensemble (BRCNE) is proposed and a simultaneous training method based on Bayesian linear regression is dev... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Bayesian Learning for Spatial Filtering in an EEG-Based Brain–Computer Interface

    Publication Year: 2013 , Page(s): 1049 - 1060
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (553 KB) |  | HTML iconHTML  

    Spatial filtering for EEG feature extraction and classification is an important tool in brain-computer interface. However, there is generally no established theory that links spatial filtering directly to Bayes classification error. To address this issue, this paper proposes and studies a Bayesian analysis theory for spatial filtering in relation to Bayes error. Following the maximum entropy princ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Neural Network-Based Optimal Adaptive Output Feedback Control of a Helicopter UAV

    Publication Year: 2013 , Page(s): 1061 - 1073
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (639 KB) |  | HTML iconHTML  

    Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The outp... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Random Sampler M-Estimator Algorithm With Sequential Probability Ratio Test for Robust Function Approximation Via Feed-Forward Neural Networks

    Publication Year: 2013 , Page(s): 1074 - 1085
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (665 KB) |  | HTML iconHTML  

    This paper addresses the problem of fitting a functional model to data corrupted with outliers using a multilayered feed-forward neural network. Although it is of high importance in practical applications, this problem has not received careful attention from the neural network research community. One recent approach to solving this problem is to use a neural network training algorithm based on the... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Incorporating Privileged Information Through Metric Learning

    Publication Year: 2013 , Page(s): 1086 - 1098
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (452 KB) |  | HTML iconHTML  

    In some pattern analysis problems, there exists expert knowledge, in addition to the original data involved in the classification process. The vast majority of existing approaches simply ignore such auxiliary (privileged) knowledge. Recently a new paradigm-learning using privileged information-was introduced in the framework of SVM+. This approach is formulated for binary classification and, as ty... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Novel Range-Free Localization Based on Multidimensional Support Vector Regression Trained in the Primal Space

    Publication Year: 2013 , Page(s): 1099 - 1113
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (867 KB) |  | HTML iconHTML  

    A novel range-free localization algorithm based on the multidimensional support vector regression (MSVR) is proposed in this paper. The range-free localization problem is formulated as a multidimensional regression problem, and a new MSVR training method is proposed to solve the regression problem. Unlike standard support vector regression, the proposed MSVR allows multiple outputs and localizes t... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Exponential H Synchronization and State Estimation for Chaotic Systems Via a Unified Model

    Publication Year: 2013 , Page(s): 1114 - 1126
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (6077 KB) |  | HTML iconHTML  

    In this paper, H synchronization and state estimation problems are considered for different types of chaotic systems. A unified model consisting of a linear dynamic system and a bounded static nonlinear operator is employed to describe these chaotic systems, such as Hopfield neural networks, cellular neural networks, Chua's circuits, unified chaotic systems, Qi systems, chaotic ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Quadratically Constrained MAP Classifier Using the Mixture of Gaussians Models as a Weight Function

    Publication Year: 2013 , Page(s): 1127 - 1140
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (443 KB) |  | HTML iconHTML  

    In this paper, we propose classifiers derived from quadratically constrained maximum a posteriori (QCMAP) estimation. The QCMAP consists of the maximization of the expectation of a cost function, which is derived from the maximum a posteriori probability and a quadratic constraint. This criterion is highly general since its forms include least squares regressions and a support vector machine. Furt... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Pinning Consensus in Networks of Multiagents via a Single Impulsive Controller

    Publication Year: 2013 , Page(s): 1141 - 1149
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1377 KB) |  | HTML iconHTML  

    In this paper, we discuss pinning consensus in networks of multiagents via impulsive controllers. In particular, we consider the case of using only one impulsive controller. We provide a sufficient condition to pin the network to a prescribed value. It is rigorously proven that in case the underlying graph of the network has spanning trees, the network can reach consensus on the prescribed value w... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Robust Adaptive Dynamic Programming With an Application to Power Systems

    Publication Year: 2013 , Page(s): 1150 - 1156
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (419 KB) |  | HTML iconHTML  

    This brief presents a novel framework of robust adaptive dynamic programming (robust-ADP) aimed at computing globally stabilizing and suboptimal control policies in the presence of dynamic uncertainties. A key strategy is to integrate ADP theory with techniques in modern nonlinear control with a unique objective of filling up a gap in the past literature of ADP without taking into account dynamic ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Structure of Indicator Function Classes With Finite Vapnik–Chervonenkis Dimensions

    Publication Year: 2013 , Page(s): 1156 - 1160
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (180 KB) |  | HTML iconHTML  

    The Vapnik-Chervonenkis (VC) dimension is used to measure the complexity of a function class and plays an important role in a variety of fields, including artificial neural networks and machine learning. One major concern is the relationship between the VC dimension and inherent characteristics of the corresponding function class. According to Sauer's lemma, if the VC dimension of an indicator fun... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Approximating Gaussian Mixture Model or Radial Basis Function Network With Multilayer Perceptron

    Publication Year: 2013 , Page(s): 1161 - 1166
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (461 KB) |  | HTML iconHTML  

    Gaussian mixture models (GMMs) and multilayer perceptron (MLP) are both popular pattern classification techniques. This brief shows that a multilayer perceptron with quadratic inputs (MLPQ) can accurately approximate GMMs with diagonal covariance matrices. The mapping equations between the parameters of GMM and the weights of MLPQ are presented. A similar approach is applied to radial basis functi... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Novel Approach to the Problem of Non-uniqueness of the Solution in Hierarchical Clustering

    Publication Year: 2013 , Page(s): 1166 - 1173
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (539 KB) |  | HTML iconHTML  

    The existence of multiple solutions in clustering, and in hierarchical clustering in particular, is often ignored in practical applications. However, this is a non-trivial problem, as different data orderings can result in different cluster sets that, in turns, may lead to different interpretations of the same data. The method presented here offers a solution to this issue. It is based on the defi... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • 2014 IEEE World Congress on Computational Intelligence

    Publication Year: 2013 , Page(s): 1174
    Save to Project icon | Request Permissions | PDF file iconPDF (3429 KB)  
    Freely Available from IEEE
  • Do what you do better with What's New @ IEEE Xplore

    Publication Year: 2013 , Page(s): 1175
    Save to Project icon | Request Permissions | PDF file iconPDF (424 KB)  
    Freely Available from IEEE
  • Together, we are advancing technology

    Publication Year: 2013 , Page(s): 1176
    Save to Project icon | Request Permissions | PDF file iconPDF (398 KB)  
    Freely Available from IEEE
  • IEEE Computational Intelligence Society Information

    Publication Year: 2013 , Page(s): C3
    Save to Project icon | Request Permissions | PDF file iconPDF (125 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Neural Networks information for authors

    Publication Year: 2013 , Page(s): C4
    Save to Project icon | Request Permissions | PDF file iconPDF (127 KB)  
    Freely Available from IEEE

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
Derong Liu
Institute of Automation
Chinese Academy of Sciences