By Topic

# IEEE Transactions on Neural Networks

## Filter Results

Displaying Results 1 - 21 of 21

Publication Year: 2008, Page(s):C1 - C4
| PDF (37 KB)
• ### IEEE Transactions on Neural Networks publication information

Publication Year: 2008, Page(s): C2
| PDF (36 KB)
• ### Dynamics of Learning in Multilayer Perceptrons Near Singularities

Publication Year: 2008, Page(s):1313 - 1328
Cited by:  Papers (11)
| | PDF (2066 KB) | HTML

The dynamical behavior of learning is known to be very slow for the multilayer perceptron, being often trapped in the "plateau." It has been recently understood that this is due to the singularity in the parameter space of perceptrons, in which trajectories of learning are drawn. The space is Riemannian from the point of view of information geometry and contains singular regions where the Riemanni... View full abstract»

• ### Robust State Estimation for Uncertain Neural Networks With Time-Varying Delay

Publication Year: 2008, Page(s):1329 - 1339
Cited by:  Papers (112)
| | PDF (786 KB) | HTML

The robust state estimation problem for a class of uncertain neural networks with time-varying delay is studied in this paper. The parameter uncertainties are assumed to be norm bounded. Based on a new bounding technique, a sufficient condition is presented to guarantee the existence of the desired state estimator for the uncertain delayed neural networks. The criterion is dependent on the size of... View full abstract»

• ### A Novel Recurrent Neural Network for Solving Nonlinear Optimization Problems With Inequality Constraints

Publication Year: 2008, Page(s):1340 - 1353
Cited by:  Papers (68)
| | PDF (708 KB) | HTML

This paper presents a novel recurrent neural network for solving nonlinear optimization problems with inequality constraints. Under the condition that the Hessian matrix of the associated Lagrangian function is positive semidefinite, it is shown that the proposed neural network is stable at a Karush-Kuhn-Tucker point in the sense of Lyapunov and its output trajectory is globally convergent to a mi... View full abstract»

• ### Individual Stable Space: An Approach to Face Recognition Under Uncontrolled Conditions

Publication Year: 2008, Page(s):1354 - 1368
Cited by:  Papers (4)
| | PDF (1745 KB) | HTML

There usually exist many kinds of variations in face images taken under uncontrolled conditions, such as changes of pose, illumination, expression, etc. Most previous works on face recognition (FR) focus on particular variations and usually assume the absence of others. Instead of such a ldquodivide and conquerrdquo strategy, this paper attempts to directly address face recognition ... View full abstract»

• ### Reinforcement-Learning-Based Dual-Control Methodology for Complex Nonlinear Discrete-Time Systems With Application to Spark Engine EGR Operation

Publication Year: 2008, Page(s):1369 - 1388
Cited by:  Papers (12)
| | PDF (1579 KB) | HTML

A novel reinforcement-learning-based dual-control methodology adaptive neural network (NN) controller is developed to deliver a desired tracking performance for a class of complex feedback nonlinear discrete-time systems, which consists of a second-order nonlinear discrete-time system in nonstrict feedback form and an affine nonlinear discrete-time system, in the presence of bounded and unknown di... View full abstract»

• ### A New Approach to Knowledge-Based Design of Recurrent Neural Networks

Publication Year: 2008, Page(s):1389 - 1401
Cited by:  Papers (3)
| | PDF (463 KB) | HTML

A major drawback of artificial neural networks (ANNs) is their black-box character. This is especially true for recurrent neural networks (RNNs) because of their intricate feedback connections. In particular, given a problem and some initial information concerning its solution, it is not at all obvious how to design an RNN that is suitable for solving this problem. In this paper, we consider a fuz... View full abstract»

• ### A Neural Model for Compensation of Sensory Abnormalities in Autism Through Feedback From a Measure of Global Perception

Publication Year: 2008, Page(s):1402 - 1414
Cited by:  Papers (2)
| | PDF (395 KB) | HTML

Sensory abnormalities and weak central coherence (WCC), a processing bias for features and local information, are important characteristics associated with autism. This paper introduces a self-organizing map (SOM)-based computational model of sensory abnormalities in autism, and of a feedback system to compensate for them. Feedback relies on a measure of balance of coverage over four (sensory) dom... View full abstract»

• ### The $Q$ -Norm Complexity Measure and the Minimum Gradient Method: A Novel Approach to the Machine Learning Structural Risk Minimization Problem

Publication Year: 2008, Page(s):1415 - 1430
Cited by:  Papers (22)
| | PDF (762 KB) | HTML

This paper presents a novel approach for dealing with the structural risk minimization (SRM) applied to a general setting of the machine learning problem. The formulation is based on the fundamental concept that supervised learning is a bi-objective optimization problem in which two conflicting objectives should be minimized. The objectives are related to the empirical training error and the machi... View full abstract»

• ### Configuration of Continuous Piecewise-Linear Neural Networks

Publication Year: 2008, Page(s):1431 - 1445
Cited by:  Papers (13)
| | PDF (919 KB) | HTML

The problem of constructing a general continuous piecewise-linear neural network is considered in this paper. It is shown that every projection domain of an arbitrary continuous piecewise-linear function can be partitioned into convex polyhedra by using difference functions of its local linear functions. Based on these convex polyhedra, a group of continuous piecewise-linear basis functions are fo... View full abstract»

• ### Training Hard-Margin Support Vector Machines Using Greedy Stagewise Algorithm

Publication Year: 2008, Page(s):1446 - 1455
Cited by:  Papers (6)
| | PDF (696 KB) | HTML

Hard-margin support vector machines (HM-SVMs) suffer from getting overfitting in the presence of noise. Soft-margin SVMs deal with this problem by introducing a regularization term and obtain a state-of-the-art performance. However, this disposal leads to a relatively high computational cost. In this paper, an alternative method, greedy stagewise algorithm for SVMs, named GS-SVMs, is presented to ... View full abstract»

• ### Deterministic Learning for Maximum-Likelihood Estimation Through Neural Networks

Publication Year: 2008, Page(s):1456 - 1467
Cited by:  Papers (5)
| | PDF (574 KB) | HTML

In this paper, a general method for the numerical solution of maximum-likelihood estimation (MLE) problems is presented; it adopts the deterministic learning (DL) approach to find close approximations to ML estimator functions for the unknown parameters of any given density. The method relies on the choice of a proper neural network and on the deterministic generation of samples of observations of... View full abstract»

• ### Visualization of Tree-Structured Data Through Generative Topographic Mapping

Publication Year: 2008, Page(s):1468 - 1493
Cited by:  Papers (15)
| | PDF (3406 KB) | HTML

In this paper, we present a probabilistic generative approach for constructing topographic maps of tree-structured data. Our model defines a low-dimensional manifold of local noise models, namely, (hidden) Markov tree models, induced by a smooth mapping from low-dimensional latent space. We contrast our approach with that of topographic map formation using recursive neural-based techniques, namely... View full abstract»

• ### Comments on "The Extreme Learning Machine

Publication Year: 2008, Page(s):1494 - 1495
Cited by:  Papers (18)
| | PDF (65 KB) | HTML

This comment letter points out that the essence of the ldquoextreme learning machine (ELM)rdquo recently appeared has been proposed earlier by Broomhead and Lowe and Pao , and discussed by other authors. Hence, it is not necessary to introduce a new name "ELM". View full abstract»

Publication Year: 2008, Page(s):1495 - 1496
Cited by:  Papers (10)
| | PDF (79 KB) | HTML

In this reply, we refer to Wang and Wan's comments on our research publication. We found that the comment letter contains some inaccurate statements. The comment letter contains some contradictions as well. View full abstract»

• ### Comments on "Adaptive Neural Control for a Class of Nonlinearly Parametric Time-Delay Systems

Publication Year: 2008, Page(s):1496 - 1498
Cited by:  Papers (5)
| | PDF (115 KB) | HTML

In this comment, we point out an error in [1], which will show that the main result of the paper cannot be generalized for nonlinearly parametric time-delay systems considered in [1]. In [1], the authors considered the problem of the adaptive control for a class of nonlinearly parametric time-delay systems and applied successfully the proposed approach to second-order nonlinear time-delay systems ... View full abstract»

• ### Reply to “Comments on “Adaptive Neural Control for a Class of Nonlinearly Parametric Time-Delay Systems””

Publication Year: 2008, Page(s): 1498
Cited by:  Papers (2)
| | PDF (54 KB) | HTML

For original paper see D. W. C. Ho et al., ibid., vol.16, no.3, p.625-35, (2005). For original paper see S. J. Yoo et al., ibid., vol.19, no.8, p.1496-8, (2008). This paper presents the reply to "Comments on ldquoAdaptive neural control for a class of nonlinearly parametric time-delay systemsrdquordquo. View full abstract»

• ### Complexity Explained (Erdi, P.; 2008) [Book review]

Publication Year: 2008, Page(s): 1499
| PDF (27 KB) | HTML
• ### Special issue on computing with words

Publication Year: 2008, Page(s): 1500
| PDF (123 KB)
• ### IEEE Computational Intelligence Society Information

Publication Year: 2008, Page(s): C3
| PDF (34 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