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Neural Networks, IEEE Transactions on

Issue 3 • Date March 2011

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  • Table of contents

    Publication Year: 2011 , Page(s): C1
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  • IEEE Transactions on Neural Networks publication information

    Publication Year: 2011 , Page(s): C2
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  • Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals

    Publication Year: 2011 , Page(s): 337 - 346
    Cited by:  Papers (22)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (552 KB) |  | HTML iconHTML  

    Prediction intervals (PIs) have been proposed in the literature to provide more information by quantifying the level of uncertainty associated to the point forecasts. Traditional methods for construction of neural network (NN) based PIs suffer from restrictive assumptions about data distribution and massive computational loads. In this paper, we propose a new, fast, yet reliable method for the con... View full abstract»

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  • Learning Associative Memories by Error Backpropagation

    Publication Year: 2011 , Page(s): 347 - 355
    Cited by:  Papers (1)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (547 KB) |  | HTML iconHTML  

    In this paper, a method for the design of Hopfield networks, bidirectional and multidirectional associative memories with asymmetric connections, is proposed. The given patterns can be assigned as locally asymptotically stable equilibria of the network by training a single-layer feedforward network. It is shown that the robustness in respect to acceptable noise in the input of the constructed netw... View full abstract»

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  • Uniformly Stable Backpropagation Algorithm to Train a Feedforward Neural Network

    Publication Year: 2011 , Page(s): 356 - 366
    Cited by:  Papers (10)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (864 KB) |  | HTML iconHTML  

    Neural networks (NNs) have numerous applications to online processes, but the problem of stability is rarely discussed. This is an extremely important issue because, if the stability of a solution is not guaranteed, the equipment that is being used can be damaged, which can also cause serious accidents. It is true that in some research papers this problem has been considered, but this concerns con... View full abstract»

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  • Principal Curve Algorithms for Partitioning High-Dimensional Data Spaces

    Publication Year: 2011 , Page(s): 367 - 380
    Cited by:  Papers (5)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1200 KB) |  | HTML iconHTML  

    Most partitioning algorithms iteratively partition a space into cells that contain underlying linear or nonlinear structures using linear partitioning strategies. The compactness of each cell depends on how well the (locally) linear partitioning strategy approximates the intrinsic structure. To partition a compact structure for complex data in a nonlinear context, this paper proposes a nonlinear p... View full abstract»

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  • On Attracting Basins of Multiple Equilibria of a Class of Cellular Neural Networks

    Publication Year: 2011 , Page(s): 381 - 394
    Cited by:  Papers (6)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (862 KB) |  | HTML iconHTML  

    In this paper, we study the distribution of attraction basins of multiple equilibrium points of cellular neural networks (CNNs). Under several conditions, the boundaries of the attracting basins of the stable equilibria of a completely stable CNN system are composed of the closures of the stable manifolds of unstable equilibria of (n - 1) dimensions. As demonstrations of this idea, under the condi... View full abstract»

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  • The Margitron: A Generalized Perceptron With Margin

    Publication Year: 2011 , Page(s): 395 - 407
    Cited by:  Papers (1)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (432 KB) |  | HTML iconHTML  

    We identify the classical perceptron algorithm with margin as a member of a broader family of large margin classifiers, which we collectively call the margitron. The margitron, (despite its) sharing the same update rule with the perceptron, is shown in an incremental setting to converge in a finite number of updates to solutions possessing any desirable fraction of the maximum margin. We also repo... View full abstract»

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  • Offline Modeling for Product Quality Prediction of Mineral Processing Using Modeling Error PDF Shaping and Entropy Minimization

    Publication Year: 2011 , Page(s): 408 - 419
    Cited by:  Papers (8)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1127 KB) |  | HTML iconHTML  

    This paper presents a novel offline modeling for product quality prediction of mineral processing which consists of a number of unit processes in series. The prediction of the product quality of the whole mineral process (i.e., the mixed concentrate grade) plays an important role and the establishment of its predictive model is a key issue for the plantwide optimization. For this purpose, a hybrid... View full abstract»

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  • Semisupervised Learning Using Negative Labels

    Publication Year: 2011 , Page(s): 420 - 432
    Cited by:  Papers (1)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (590 KB) |  | HTML iconHTML  

    The problem of semisupervised learning has aroused considerable research interests in the past few years. Most of these methods aim to learn from a partially labeled dataset, i.e., they assume that the exact labels of some data are already known. In this paper, we propose to use a novel type of supervision information to guide the process of semisupervised learning, which indicates whether a point... View full abstract»

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  • Efficient Sparse Generalized Multiple Kernel Learning

    Publication Year: 2011 , Page(s): 433 - 446
    Cited by:  Papers (17)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (520 KB) |  | HTML iconHTML  

    Kernel methods have been successfully applied in various applications. To succeed in these applications, it is crucial to learn a good kernel representation, whose objective is to reveal the data similarity precisely. In this paper, we address the problem of multiple kernel learning (MKL), searching for the optimal kernel combination weights through maximizing a generalized performance measure. Mo... View full abstract»

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  • MDS-Based Multiresolution Nonlinear Dimensionality Reduction Model for Color Image Segmentation

    Publication Year: 2011 , Page(s): 447 - 460
    Cited by:  Papers (6)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1096 KB) |  | HTML iconHTML  

    In this paper, we present an efficient coarse-to-fine multiresolution framework for multidimensional scaling and demonstrate its performance on a large-scale nonlinear dimensionality reduction and embedding problem in a texture feature extraction step for the unsupervised image segmentation problem. We demonstrate both the efficiency of our multiresolution algorithm and its real interest to learn ... View full abstract»

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  • Video Time Encoding Machines

    Publication Year: 2011 , Page(s): 461 - 473
    Cited by:  Papers (9)  |  Patents (1)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1300 KB) |  | HTML iconHTML  

    We investigate architectures for time encoding and time decoding of visual stimuli such as natural and synthetic video streams (movies, animation). The architecture for time encoding is akin to models of the early visual system. It consists of a bank of filters in cascade with single-input multi-output neural circuits. Neuron firing is based on either a threshold-and-fire or an integrate-and-fire ... View full abstract»

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  • Topology-Based Hierarchical Clustering of Self-Organizing Maps

    Publication Year: 2011 , Page(s): 474 - 485
    Cited by:  Papers (12)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1154 KB) |  | HTML iconHTML  

    A powerful method in the analysis of datasets where there are many natural clusters with varying statistics such as different sizes, shapes, density distribution, overlaps, etc., is the use of self-organizing maps (SOMs). However, further processing tools, such as visualization and interactive clustering, are often necessary to capture the clusters from the learned SOM knowledge. A recent visualiz... View full abstract»

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  • Distributed State Estimation for Discrete-Time Sensor Networks With Randomly Varying Nonlinearities and Missing Measurements

    Publication Year: 2011 , Page(s): 486 - 496
    Cited by:  Papers (18)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (363 KB) |  | HTML iconHTML  

    This paper deals with the distributed state estimation problem for a class of sensor networks described by discrete-time stochastic systems with randomly varying nonlinearities and missing measurements. In the sensor network, there is no centralized processor capable of collecting all the measurements from the sensors, and therefore each individual sensor needs to estimate the system state based n... View full abstract»

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  • Real-Time Recurrent Neural State Estimation

    Publication Year: 2011 , Page(s): 497 - 505
    Cited by:  Papers (8)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (625 KB) |  | HTML iconHTML  

    A nonlinear discrete-time neural observer for discrete-time unknown nonlinear systems in presence of external disturbances and parameter uncertainties is presented. It is based on a discrete-time recurrent high-order neural network trained with an extended Kalman-filter based algorithm. This brief includes the stability proof based on the Lyapunov approach. The applicability of the proposed scheme... View full abstract»

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  • BELM: Bayesian Extreme Learning Machine

    Publication Year: 2011 , Page(s): 505 - 509
    Cited by:  Papers (6)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (415 KB) |  | HTML iconHTML  

    The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high... View full abstract»

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  • Call for papers IEEE Transactions on Neural Networks Special Issue: Online Learning in Kernel Methods

    Publication Year: 2011 , Page(s): 510
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    Publication Year: 2011 , Page(s): 511
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    Publication Year: 2011 , Page(s): 512
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2011 , Page(s): C3
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  • IEEE Transactions on Neural Networks Information for authors

    Publication Year: 2011 , Page(s): C4
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Aims & Scope

IEEE Transactions on Neural Networks is devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware.

 

This Transactions ceased production in 2011. The current retitled publication is IEEE Transactions on Neural Networks and Learning Systems.

Full Aims & Scope