Special Notice
IEEE Xplore is transitioning to HTTPS on 9 April 2018. Customer access via EZproxy will require version 6 or higher with TLS 1.1 or 1.2 enabled.
Review our EZproxy Upgrade Checklist to ensure uninterrupted access.

# IEEE Transactions on Neural Networks

## Filter Results

Displaying Results 1 - 22 of 22

Publication Year: 2008, Page(s): C1
| PDF (37 KB)
• ### IEEE Transactions on Neural Networks publication information

Publication Year: 2008, Page(s): C2
| PDF (39 KB)
• ### A Nonfeasible Gradient Projection Recurrent Neural Network for Equality-Constrained Optimization Problems

Publication Year: 2008, Page(s):1665 - 1677
Cited by:  Papers (11)
| | PDF (473 KB) | HTML

In this paper, a recurrent neural network for both convex and nonconvex equality-constrained optimization problems is proposed, which makes use of a cost gradient projection onto the tangent space of the constraints. The proposed neural network constructs a generically nonfeasible trajectory, satisfying the constraints only as t rarr infin. Local convergence results are given that do not as... View full abstract»

• ### Extrapolative Delay Compensation Through Facilitating Synapses and Its Relation to the Flash-Lag Effect

Publication Year: 2008, Page(s):1678 - 1688
Cited by:  Papers (7)
| | PDF (587 KB) | HTML

Neural conduction delay is a serious issue for organisms that need to act in real time. Various forms of flash-lag effect (FLE) suggest that the nervous system may perform extrapolation to compensate for delay. For example, in motion FLE, the position of a moving object is perceived to be ahead of a brief flash when they are actually colocalized. However, the precise mechanism for extrapolation at... View full abstract»

• ### Adaptive-Fourier-Neural-Network-Based Control for a Class of Uncertain Nonlinear Systems

Publication Year: 2008, Page(s):1689 - 1701
Cited by:  Papers (6)
| | PDF (767 KB) | HTML

An adaptive Fourier neural network (AFNN) control scheme is presented in this paper for the control of a class of uncertain nonlinear systems. Based on Fourier analysis and neural network (NN) theory, AFNN employs orthogonal complex Fourier exponentials as the activation functions. Due to the clear physical meaning of the neurons, the determination of the AFNN structure as well as the parameters o... View full abstract»

• ### Adaptive Dynamic Inversion via Time-Scale Separation

Publication Year: 2008, Page(s):1702 - 1711
Cited by:  Papers (19)
| | PDF (772 KB) | HTML

This paper presents a full state feedback adaptive dynamic inversion method for uncertain systems that depend nonlinearly upon the control input. Using a specialized set of basis functions that respect the monotonic property of the system nonlinearities with respect to control input, a state predictor is defined for derivation of the adaptive laws. The adaptive dynamic inversion controller is defi... View full abstract»

• ### Adaptive Output Feedback Control of Flexible-Joint Robots Using Neural Networks: Dynamic Surface Design Approach

Publication Year: 2008, Page(s):1712 - 1726
Cited by:  Papers (59)
| | PDF (645 KB) | HTML

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recur... View full abstract»

• ### IMORL: Incremental Multiple-Object Recognition and Localization

Publication Year: 2008, Page(s):1727 - 1738
Cited by:  Papers (29)
| | PDF (1088 KB) | HTML

This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an image. Unlike the conventional multiple-object learning algorithms, the proposed method can automatically and adaptively learn from continuous video streams over the entire learning life. This kind of incremental learning c... View full abstract»

• ### The Correspondence Between Deterministic and Stochastic Digital Neurons: Analysis and Methodology

Publication Year: 2008, Page(s):1739 - 1752
Cited by:  Papers (8)
| | PDF (1938 KB) | HTML

This paper analyzes the criteria for the direct correspondence between a deterministic neural network and its stochastic counterpart, and presents the guidelines that have been derived to establish such a correspondence during the design of a neural network application. In particular, the role of the slope and bias of the neuron activation function and that of the noise of its output have been add... View full abstract»

• ### A New Solution Path Algorithm in Support Vector Regression

Publication Year: 2008, Page(s):1753 - 1767
Cited by:  Papers (26)
| | PDF (1389 KB) | HTML

In this paper, regularization path algorithms were proposed as a novel approach to the model selection problem by exploring the path of possibly all solutions with respect to some regularization hyperparameter in an efficient way. This approach was later extended to a support vector regression (SVR) model called epsiv -SVR. However, the method requires that the error parameter epsiv be set a View full abstract»

• ### Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection

Publication Year: 2008, Page(s):1768 - 1782
Cited by:  Papers (70)
| | PDF (1604 KB) | HTML

High-dimensional data are common in many domains, and dimensionality reduction is the key to cope with the curse-of-dimensionality. Linear discriminant analysis (LDA) is a well-known method for supervised dimensionality reduction. When dealing with high-dimensional and low sample size data, classical LDA suffers from the singularity problem. Over the years, many algorithms have been develop... View full abstract»

• ### New Delay-Dependent Stability Results for Neural Networks With Time-Varying Delay

Publication Year: 2008, Page(s):1783 - 1791
Cited by:  Papers (57)
| | PDF (349 KB) | HTML

This paper studies the problem of stability analysis for neural networks (NNs) with a time-varying delay. Unlike the previous works, the activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. By defining a more general type of Lyapunov functionals, some new less conservative delay-dependent stability criteria are established in terms of linear matrix inequalitie... View full abstract»

• ### Pruning Support Vector Machines Without Altering Performances

Publication Year: 2008, Page(s):1792 - 1803
Cited by:  Papers (17)
| | PDF (1576 KB) | HTML

Support vector machines (SV machines, SVMs) have many merits that distinguish themselves from many other machine-learning algorithms, such as the nonexistence of local minima, the possession of the largest distance from the separating hyperplane to the SVs, and a solid theoretical foundation. However, SVM training algorithms such as the efficient sequential minimal optimization (SMO) often produce... View full abstract»

• ### Quasi-Lagrangian Neural Network for Convex Quadratic Optimization

Publication Year: 2008, Page(s):1804 - 1809
Cited by:  Papers (8)
| | PDF (437 KB) | HTML

A new neural network for convex quadratic optimization is presented in this brief. The proposed network can handle both equality and inequality constraints, as well as bound constraints on the optimization variables. It is based on the Lagrangian approach, but exploits a partial dual method in order to keep the number of variables at minimum. The dynamic evolution is globally convergent and the st... View full abstract»

• ### Global $mu$ -Synchronization of Linearly Coupled Unbounded Time-Varying Delayed Neural Networks With Unbounded Delayed Coupling

Publication Year: 2008, Page(s):1809 - 1816
Cited by:  Papers (21)
| | PDF (562 KB) | HTML

In this brief, we study the global synchronization of linearly coupled neural networks with delayed couplings, where the intrinsic systems are recurrently connected neural networks with unbounded time-varying delays, and the couplings include instant couplings and unbounded delayed couplings. The concept of mu-synchronization is introduced. Some sufficient conditions are derived for the global mu-... View full abstract»

• ### Boltzmann Machines Reduction by High-Order Decimation

Publication Year: 2008, Page(s):1816 - 1821
Cited by:  Papers (1)
| | PDF (235 KB) | HTML

Decimation is a common technique in statistical physics that is used in the context of Boltzmann machines (BMs) to drastically reduce the computational cost at the learning stage. Decimation allows to analytically evaluate quantities that should otherwise be statistically estimated by means of Monte Carlo (MC) simulations. However, in its original formulation, this method could only be applied to ... View full abstract»

• ### Matrix-Variate Factor Analysis and Its Applications

Publication Year: 2008, Page(s):1821 - 1826
Cited by:  Papers (10)
| | PDF (599 KB) | HTML

Factor analysis (FA) seeks to reveal the relationship between an observed vector variable and a latent variable of reduced dimension. It has been widely used in many applications involving high-dimensional data, such as image representation and face recognition. An intrinsic limitation of FA lies in its potentially poor performance when the data dimension is high, a problem known as curse of dimen... View full abstract»

• ### Nonlinear Knowledge-Based Classification

Publication Year: 2008, Page(s):1826 - 1832
Cited by:  Papers (23)
| | PDF (1023 KB) | HTML

In this brief, prior knowledge over general nonlinear sets is incorporated into nonlinear kernel classification problems as linear constraints in a linear program. These linear constraints are imposed at arbitrary points, not necessarily where the prior knowledge is given. The key tool in this incorporation is a theorem of the alternative for convex functions that converts nonlinear prior knowledg... View full abstract»

• ### Local Classifier Weighting by Quadratic Programming

Publication Year: 2008, Page(s):1832 - 1838
Cited by:  Papers (17)  |  Patents (1)
| | PDF (545 KB) | HTML

It has been widely accepted that the classification accuracy can be improved by combining outputs of multiple classifiers. However, how to combine multiple classifiers with various (potentially conflicting) decisions is still an open problem. A rich collection of classifier combination procedures-many of which are heuristic in nature-have been developed for this goal. In this brief, we describe a ... View full abstract»

• ### Nonlinear Dynamic Modeling of Physiological Systems (Marmarellis, V.Z.; 2004) [Book review]

Publication Year: 2008, Page(s):1839 - 1840
| PDF (33 KB)
• ### IEEE Computational Intelligence Society Information

Publication Year: 2008, Page(s): C3
| PDF (35 KB)
• ### Blank page [back cover]

Publication Year: 2008, Page(s): C4
| PDF (5 KB)

## 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