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# IEEE Transactions on Neural Networks

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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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• ### Data Visualization and Dimensionality Reduction Using Kernel Maps With a Reference Point

Publication Year: 2008, Page(s):1501 - 1517
Cited by:  Papers (18)
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In this paper, a new kernel-based method for data visualization and dimensionality reduction is proposed. A reference point is considered corresponding to additional constraints taken in the problem formulation. In contrast with the class of kernel eigenmap methods, the solution (coordinates in the low-dimensional space) is characterized by a linear system instead of an eigenvalue problem. The ker... View full abstract»

• ### Decision Manifolds—A Supervised Learning Algorithm Based on Self-Organization

Publication Year: 2008, Page(s):1518 - 1530
Cited by:  Papers (7)
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In this paper, we present a neural classifier algorithm that locally approximates the decision surface of labeled data by a patchwork of separating hyperplanes, which are arranged under certain topological constraints similar to those of self-organizing maps (SOMs). We take advantage of the fact that these boundaries can often be represented by linear ones connected by a low-dimensional nonlinear ... View full abstract»

• ### Hybrid Multiobjective Evolutionary Design for Artificial Neural Networks

Publication Year: 2008, Page(s):1531 - 1548
Cited by:  Papers (43)
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Evolutionary algorithms are a class of stochastic search methods that attempts to emulate the biological process of evolution, incorporating concepts of selection, reproduction, and mutation. In recent years, there has been an increase in the use of evolutionary approaches in the training of artificial neural networks (ANNs). While evolutionary techniques for neural networks have shown to provide ... View full abstract»

• ### A Neural-Network-Based Model for the Dynamic Simulation of the Tire/Suspension System While Traversing Road Irregularities

Publication Year: 2008, Page(s):1549 - 1563
Cited by:  Papers (13)
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This paper deals with the simulation of the tire/suspension dynamics by using recurrent neural networks (RNNs). RNNs are derived from the multilayer feedforward neural networks, by adding feedback connections between output and input layers. The optimal network architecture derives from a parametric analysis based on the optimal tradeoff between network accuracy and size. The neural network can be... View full abstract»

• ### Analysis of the Initial Values in Split-Complex Backpropagation Algorithm

Publication Year: 2008, Page(s):1564 - 1573
Cited by:  Papers (14)
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When a multilayer perceptron (MLP) is trained with the split-complex backpropagation (SCBP) algorithm, one observes a relatively strong dependence of the performance on the initial values. For the effective adjustments of the weights and biases in SCBP, we propose that the range of the initial values should be greater than that of the adjustment quantities. This criterion can reduce the misadjustm... View full abstract»

• ### An Instance-Based Algorithm With Auxiliary Similarity Information for the Estimation of Gait Kinematics From Wearable Sensors

Publication Year: 2008, Page(s):1574 - 1582
Cited by:  Papers (11)
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Wearable human movement measurement systems are increasingly popular as a means of capturing human movement data in real-world situations. Previous work has attempted to estimate segment kinematics during walking from foot acceleration and angular velocity data. In this paper, we propose a novel neural network [GRNN with Auxiliary Similarity Information (GASI)] that est... View full abstract»

• ### Kernel Component Analysis Using an Epsilon-Insensitive Robust Loss Function

Publication Year: 2008, Page(s):1583 - 1598
Cited by:  Papers (27)
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Kernel principal component analysis (PCA) is a technique to perform feature extraction in a high-dimensional feature space, which is nonlinearly related to the original input space. The kernel PCA formulation corresponds to an eigendecomposition of the kernel matrix: eigenvectors with large eigenvalues correspond to the principal components in the feature space. Starting from the least squares sup... View full abstract»

• ### Adaptive Predictive Control Using Neural Network for a Class of Pure-Feedback Systems in Discrete Time

Publication Year: 2008, Page(s):1599 - 1614
Cited by:  Papers (100)
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In this paper, adaptive neural network (NN) control is investigated for a class of nonlinear pure-feedback discrete-time systems. By using prediction functions of future states, the pure-feedback system is transformed into an n-step-ahead predictor, based on which state feedback NN control is synthesized. Next, by investigating the relationship between outputs and states, the system is tran... View full abstract»

• ### Feedback-Linearization-Based Neural Adaptive Control for Unknown Nonaffine Nonlinear Discrete-Time Systems

Publication Year: 2008, Page(s):1615 - 1625
Cited by:  Papers (32)
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A new feedback-linearization-based neural network (NN) adaptive control is proposed for unknown nonaffine nonlinear discrete-time systems. An equivalent model in afflne-like form is first derived for the original nonaffine discrete-time systems as feedback linearization methods cannot be implemented for such systems. Then, feedback linearization adaptive control is implemented based on the affine-... View full abstract»

• ### Training Spiking Neuronal Networks With Applications in Engineering Tasks

Publication Year: 2008, Page(s):1626 - 1640
Cited by:  Papers (16)
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In this paper, spiking neuronal models employing means, variances, and correlations for computation are introduced. We present two approaches in the design of spiking neuronal networks, both of which are applied to engineering tasks. In exploring the input-output relationship of integrate-and-fire (IF) neurons with Poisson inputs, we are able to define mathematically robust learning rules, which c... View full abstract»

• ### A Hybrid ART-GRNN Online Learning Neural Network With a $varepsilon$ -Insensitive Loss Function

Publication Year: 2008, Page(s):1641 - 1646
Cited by:  Papers (19)
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In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical stu... View full abstract»

• ### Delay-Dependent Stability for Recurrent Neural Networks With Time-Varying Delays

Publication Year: 2008, Page(s):1647 - 1651
Cited by:  Papers (73)
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This brief is concerned with the stability for static neural networks with time-varying delays. Delay-independent conditions are proposed to ensure the asymptotic stability of the neural network. The delay-independent conditions are less conservative than existing ones. To further reduce the conservatism, delay-dependent conditions are also derived, which can be applied to fast time-varying delays... View full abstract»

• ### A Fast and Scalable Recurrent Neural Network Based on Stochastic Meta Descent

Publication Year: 2008, Page(s):1652 - 1658
Cited by:  Papers (5)
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This brief presents an efficient and scalable online learning algorithm for recurrent neural networks (RNNs). The approach is based on the real-time recurrent learning (RTRL) algorithm, whereby the sensitivity set of each neuron is reduced to weights associated with either its input or output links. This yields a reduced storage and computational complexity of O(N2). Stochastic meta des... View full abstract»

• ### Symmetric Complex-Valued RBF Receiver for Multiple-Antenna-Aided Wireless Systems

Publication Year: 2008, Page(s):1659 - 1665
Cited by:  Papers (14)
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A nonlinear beamforming assisted detector is proposed for multiple-antenna-aided wireless systems employing complex-valued quadrature phase shift-keying modulation. By exploiting the inherent symmetry of the optimal Bayesian detection solution, a novel complex-valued symmetric radial basis function (SRBF)-network-based detector is developed, which is capable of approaching the optimal Bayesian per... View full abstract»

• ### Call for Papers 2009 International Joint Conference on Neural Networks-IJCNN2009

Publication Year: 2008, Page(s): 1664
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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