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# IEEE Transactions on Neural Networks

## Filter Results

Displaying Results 1 - 19 of 19

Publication Year: 2008, Page(s): C1
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• ### IEEE Transactions on Neural Networks publication information

Publication Year: 2008, Page(s): C2
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• ### Stochastic Resonance in Continuous and Spiking Neuron Models With Levy Noise

Publication Year: 2008, Page(s):1993 - 2008
Cited by:  Papers (42)
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Levy noise can help neurons detect faint or subthreshold signals. Levy noise extends standard Brownian noise to many types of impulsive jump-noise processes found in real and model neurons as well as in models of finance and other random phenomena. Two new theorems and the ItÔ calculus show that white Levy noise will benefit subthreshold neuronal signal detection if the noise process... View full abstract»

• ### Equivalence Probability and Sparsity of Two Sparse Solutions in Sparse Representation

Publication Year: 2008, Page(s):2009 - 2021
Cited by:  Papers (16)
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This paper discusses the estimation and numerical calculation of the probability that the 0-norm and 1-norm solutions of underdetermined linear equations are equivalent in the case of sparse representation. First, we define the sparsity degree of a signal. Two equivalence probability estimates are obtained when the entries of the 0-norm solution have different sparsity degrees. One is for t... View full abstract»

• ### An Improved Dual Neural Network for Solving a Class of Quadratic Programming Problems and Its $k$-Winners-Take-All Application

Publication Year: 2008, Page(s):2022 - 2031
Cited by:  Papers (84)
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This paper presents a novel recurrent neural network for solving a class of convex quadratic programming (QP) problems, in which the quadratic term in the objective function is the square of the Euclidean norm of the variable. This special structure leads to a set of simple optimality conditions for the problem, based on which the neural network model is formulated. Compared with existing n... View full abstract»

• ### Multilayer Potts Perceptrons With Levenberg–Marquardt Learning

Publication Year: 2008, Page(s):2032 - 2043
Cited by:  Papers (21)
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This paper presents learning multilayer Potts perceptrons (MLPotts) for data driven function approximation. A Potts perceptron is composed of a receptive field and a $K$ -state transfer function that is generalized from sigmoid-like transfer functions of traditional perceptrons. An MLPotts network is organized to perform tran... View full abstract»

• ### Nesting One-Against-One Algorithm Based on SVMs for Pattern Classification

Publication Year: 2008, Page(s):2044 - 2052
Cited by:  Papers (35)
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Support vector machines (SVMs), which were originally designed for binary classifications, are an excellent tool for machine learning. For the multiclass classifications, they are usually converted into binary ones before they can be used to classify the examples. In the one-against-one algorithm with SVMs, there exists an unclassifiable region where the data samples cannot be classified by... View full abstract»

• ### Just-in-Time Adaptive Classifiers—Part II: Designing the Classifier

Publication Year: 2008, Page(s):2053 - 2064
Cited by:  Papers (34)
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Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very f... View full abstract»

• ### Topology Preservation and Cooperative Learning in Identification of Multiple Model Systems

Publication Year: 2008, Page(s):2065 - 2072
Cited by:  Papers (9)
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The self-organizing network (SON)-based multiple model system is a recently proposed method for identifying the dynamics of a general nonlinear system. It has been observed by researchers that cooperative learning among neighboring regions is sometimes important for the success of identification of a nonlinear system under the multiple model system framework. In this paper, we intend to for... View full abstract»

• ### Neural-Network-Based State Feedback Control of a Nonlinear Discrete-Time System in Nonstrict Feedback Form

Publication Year: 2008, Page(s):2073 - 2087
Cited by:  Papers (32)
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In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired tracking performance for the control of an unknown, second-order, nonlinear discrete-time system expressed in nonstrict feedback form. In the first approach, two feedforward NNs are employed in the controller with tracking error as the feedback variable whereas in the View full abstract»

• ### Color Image Discriminant Models and Algorithms for Face Recognition

Publication Year: 2008, Page(s):2088 - 2098
Cited by:  Papers (59)
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This paper presents a basic color image discriminant (CID) model and its general version for color image recognition. The CID models seek to unify the color image representation and recognition tasks into one framework. The proposed models, therefore, involve two sets of variables: a set of color component combination coefficients for color image representation and one or multiple projectio... View full abstract»

• ### A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks

Publication Year: 2008, Page(s):2099 - 2115
Cited by:  Papers (128)
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In this paper, a novel heuristic structure optimization methodology for radial basis probabilistic neural networks (RBPNNs) is proposed. First, a minimum volume covering hyperspheres (MVCH) algorithm is proposed to select the initial hidden-layer centers of the RBPNN, and then the recursive orthogonal least square algorithm (ROLSA) combined with the particle swarm optimization (PSO) algorit... View full abstract»

• ### A Kernel-Induced Space Selection Approach to Model Selection in KLDA

Publication Year: 2008, Page(s):2116 - 2131
Cited by:  Papers (20)  |  Patents (1)
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Model selection in kernel linear discriminant analysis (KLDA) refers to the selection of appropriate parameters of a kernel function and the regularizer. By following the principle of maximum information preservation, this paper formulates the model selection problem as a problem of selecting an optimal kern... View full abstract»

• ### Efficient Object Recognition Using Boundary Representation and Wavelet Neural Network

Publication Year: 2008, Page(s):2132 - 2149
Cited by:  Papers (23)
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Wavelet neural networks combine the functions of time–frequency localization from the wavelet transform and of self-studying from the neural network, which make them particularly suitable for the classification of complex patterns. In this paper, an efficient object recognition method using boundary representation and the wavelet neural network is proposed. The method employs a wavel... View full abstract»

• ### Radial Basis Function Networks GPU-Based Implementation

Publication Year: 2008, Page(s):2150 - 2154
Cited by:  Papers (14)
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Neural networks (NNs) have been used in several areas, showing their potential but also their limitations. One of the main limitations is the long time required for the training process; this is not useful in the case of a fast training process being required to respond to changes in the application domain. A possible way to accelerate the learning process of an NN is to implement it in har... View full abstract»

• ### Improved Delay-Dependent Asymptotic Stability Criteria for Delayed Neural Networks

Publication Year: 2008, Page(s):2154 - 2161
Cited by:  Papers (54)
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This brief is concerned with asymptotic stability of neural networks with uncertain delays. Two types of uncertain delays are considered: one is constant while the other is time varying. The discretized Lyapunov–Krasovskii functional (LKF) method is integrated with the technique of introducing the free-weighting matrix between the terms of the Leibniz–Newton formula. The integ... View full abstract»

• ### 2008 Index IEEE Transactions on Neural Networks Vol. 19

Publication Year: 2008, Page(s):2162 - 2184
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• ### IEEE Computational Intelligence Society Information

Publication Year: 2008, Page(s): C3
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• ### Blank page [back cover]

Publication Year: 2008, 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