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Neural Networks and Learning Systems, IEEE Transactions on

Issue 11 • Date Nov. 2012

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Displaying Results 1 - 19 of 19
  • Table of contents

    Publication Year: 2012 , Page(s): C1
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  • IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

    Publication Year: 2012 , Page(s): C2
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  • Decirculation Process in Neural Network Dynamics

    Publication Year: 2012 , Page(s): 1677 - 1689
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (740 KB) |  | HTML iconHTML  

    We describe a decirculation process which marks perturbations of network structure and neural updating that are necessary for evolutionary neural networks to proceed from one circulating state to another. Two aspects of control parameters, screen updating and flow diagrams, are developed to quantify such perturbations, and hence to manage the dynamics of evolutionary neural networks. A dynamic sta... View full abstract»

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  • Robust Support Vector Regression for Uncertain Input and Output Data

    Publication Year: 2012 , Page(s): 1690 - 1700
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (445 KB) |  | HTML iconHTML  

    In this paper, a robust support vector regression (RSVR) method with uncertain input and output data is studied. First, the data uncertainties are investigated under a stochastic framework and two linear robust formulations are derived. Linear formulations robust to ellipsoidal uncertainties are also considered from a geometric perspective. Second, kernelized RSVR formulations are established for ... View full abstract»

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  • Neural-Fitted TD-Leaf Learning for Playing Othello With Structured Neural Networks

    Publication Year: 2012 , Page(s): 1701 - 1713
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (668 KB) |  | HTML iconHTML  

    This paper describes a methodology for quickly learning to play games at a strong level. The methodology consists of a novel combination of three techniques, and a variety of experiments on the game of Othello demonstrates their usefulness. First, structures or topologies in neural network connectivity patterns are used to decrease the number of learning parameters and to deal more effectively wit... View full abstract»

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  • Global Tracking Control of Strict-Feedback Systems Using Neural Networks

    Publication Year: 2012 , Page(s): 1714 - 1725
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (566 KB) |  | HTML iconHTML  

    Most existing adaptive neural controllers ensure semiglobally uniform ultimately bounded stability on the condition that the neural approximation remains valid for all time. However, such a condition is difficult to verify beforehand. As a result, deterioration of tracking performance or even instability may occur in real applications. A common recourse is to activate an extra robust controller ou... View full abstract»

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  • Unsupervised Learning of Categorical Data With Competing Models

    Publication Year: 2012 , Page(s): 1726 - 1737
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (576 KB) |  | HTML iconHTML  

    This paper considers the unsupervised learning of high-dimensional binary feature vectors representing categorical information. A cognitively inspired framework, referred to as modeling fields theory (MFT), is utilized as the basic methodology. A new MFT-based algorithm, referred to as accelerated maximum a posteriori (MAP), is proposed. Accelerated MAP allows simultaneous learning and selection o... View full abstract»

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  • Discriminative Least Squares Regression for Multiclass Classification and Feature Selection

    Publication Year: 2012 , Page(s): 1738 - 1754
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (725 KB) |  | HTML iconHTML  

    This paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to enlarge the distance between different classes under the conceptual framework of LSR. First, a technique called ε-dragging is introduced to force the regression targets of different classes moving along opposite directions such that the di... View full abstract»

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  • Decentralized Asynchronous Learning in Cellular Neural Networks

    Publication Year: 2012 , Page(s): 1755 - 1766
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (4080 KB) |  | HTML iconHTML  

    Cellular neural networks (CNNs), as previously described, consist of identical units called cells that are connected to their adjacent neighbors. These cells interact with each other in order to fulfill a common goal. The current methods involved in learning of CNNs are usually centralized (cells are trained in one location) and synchronous (all cells are trained simultaneously either sequentially... View full abstract»

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  • Boosted Network Classifiers for Local Feature Selection

    Publication Year: 2012 , Page(s): 1767 - 1778
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (4693 KB) |  | HTML iconHTML  

    Like all models, network feature selection models require that assumptions be made on the size and structure of the desired features. The most common assumption is sparsity, where only a small section of the entire network is thought to produce a specific phenomenon. The sparsity assumption is enforced through regularized models, such as the lasso. However, assuming sparsity may be inappropriate f... View full abstract»

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  • Semisupervised Classification With Cluster Regularization

    Publication Year: 2012 , Page(s): 1779 - 1792
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1200 KB) |  | HTML iconHTML  

    Semisupervised classification (SSC) learns, from cheap unlabeled data and labeled data, to predict the labels of test instances. In order to make use of the information from unlabeled data, there should be an assumed relationship between the true class structure and the data distribution. One assumption is that data points clustered together are likely to have the same class label. In this paper, ... View full abstract»

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  • Latent Feature Kernels for Link Prediction on Sparse Graphs

    Publication Year: 2012 , Page(s): 1793 - 1804
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (716 KB) |  | HTML iconHTML  

    Predicting new links in a network is a problem of interest in many application domains. Most of the prediction methods utilize information on the network's entities, such as nodes, to build a model of links. Network structures are usually not used except for networks with similarity or relatedness semantics. In this paper, we use network structures for link prediction with a more general network t... View full abstract»

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  • Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting

    Publication Year: 2012 , Page(s): 1805 - 1815
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1419 KB) |  | HTML iconHTML  

    Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit t... View full abstract»

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  • Multistability of Neural Networks With Mexican-Hat-Type Activation Functions

    Publication Year: 2012 , Page(s): 1816 - 1826
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (687 KB) |  | HTML iconHTML  

    In this paper, we are concerned with a class of neural networks with Mexican-hat-type activation functions. Due to the different structure from neural networks with saturated activation functions, a set of new sufficient conditions are presented to study the multistability, including the total number of equilibrium points, their locations, and stability. Furthermore, the attraction basins of stabl... View full abstract»

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  • Convergence Analyses on On-Line Weight Noise Injection-Based Training Algorithms for MLPs

    Publication Year: 2012 , Page(s): 1827 - 1840
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (619 KB) |  | HTML iconHTML  

    Injecting weight noise during training is a simple technique that has been proposed for almost two decades. However, little is known about its convergence behavior. This paper studies the convergence of two weight noise injection-based training algorithms, multiplicative weight noise injection with weight decay and additive weight noise injection with weight decay. We consider that they are applie... View full abstract»

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  • Time Series Modeling and Forecasting Using Memetic Algorithms for Regime-Switching Models

    Publication Year: 2012 , Page(s): 1841 - 1847
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (206 KB) |  | HTML iconHTML  

    In this brief, we present a novel model fitting procedure for the neuro-coefficient smooth transition autoregressive model (NCSTAR), as presented by Medeiros and Veiga. The model is endowed with a statistically founded iterative building procedure and can be interpreted in terms of fuzzy rule-based systems. The interpretability of the generated models and a mathematically sound building procedure ... View full abstract»

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  • IIJCNN-Dallas, TX

    Publication Year: 2012 , Page(s): 1848
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2012 , Page(s): C3
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  • IEEE Transactions on Neural Networks information for authors

    Publication Year: 2012 , 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
Derong Liu
Institute of Automation
Chinese Academy of Sciences