# IEEE Transactions on Neural Networks and Learning Systems

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Displaying Results 1 - 25 of 28

Publication Year: 2015, Page(s):C1 - 1581
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• ### IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Publication Year: 2015, Page(s): C2
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• ### Dimensionality Reduction for Hyperspectral Data Based on Class-Aware Tensor Neighborhood Graph and Patch Alignment

Publication Year: 2015, Page(s):1582 - 1593
Cited by:  Papers (7)
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To take full advantage of hyperspectral information, to avoid data redundancy and to address the curse of dimensionality concern, dimensionality reduction (DR) becomes particularly important to analyze hyperspectral data. Exploring the tensor characteristic of hyperspectral data, a DR algorithm based on class-aware tensor neighborhood graph and patch alignment is proposed here. First, hyperspectra... View full abstract»

• ### Self-Organizing Map With Time-Varying Structure to Plan and Control Artificial Locomotion

Publication Year: 2015, Page(s):1594 - 1607
Cited by:  Papers (1)
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This paper presents an algorithm, self-organizing map-state trajectory generator (SOM-STG), to plan and control legged robot locomotion. The SOM-STG is based on an SOM with a time-varying structure characterized by constructing autonomously close-state trajectories from an arbitrary number of robot postures. Each trajectory represents a cyclical movement of the limbs of an animal. The SOM-STG was ... View full abstract»

• ### Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

Publication Year: 2015, Page(s):1608 - 1621
Cited by:  Papers (4)
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A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a loc... View full abstract»

• ### Learning a Probabilistic Topology Discovering Model for Scene Categorization

Publication Year: 2015, Page(s):1622 - 1634
Cited by:  Papers (4)
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A recent advance in scene categorization prefers a topological based modeling to capture the existence and relationships among different scene components. To that effect, local features are typically used to handle photographing variances such as occlusions and clutters. However, in many cases, the local features alone cannot well capture the scene semantics since they are extracted from tiny regi... View full abstract»

• ### A Convex Geometry-Based Blind Source Separation Method for Separating Nonnegative Sources

Publication Year: 2015, Page(s):1635 - 1644
Cited by:  Papers (4)
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This paper presents a convex geometry (CG)-based method for blind separation of nonnegative sources. First, the unaccessible source matrix is normalized to be column-sum-to-one by mapping the available observation matrix. Then, its zero-samples are found by searching the facets of the convex hull spanned by the mapped observations. Considering these zero-samples, a quadratic cost function with res... View full abstract»

• ### Approximate $N$ -Player Nonzero-Sum Game Solution for an Uncertain Continuous Nonlinear System

Publication Year: 2015, Page(s):1645 - 1658
Cited by:  Papers (4)
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An approximate online equilibrium solution is developed for an N-player nonzero-sum game subject to continuous-time nonlinear unknown dynamics and an infinite horizon quadratic cost. A novel actor-critic-identifier structure is used, wherein a robust dynamic neural network is used to asymptotically identify the uncertain system with additive disturbances, and a set of critic and actor NNs are used... View full abstract»

• ### A Hybrid Constructive Algorithm for Single-Layer Feedforward Networks Learning

Publication Year: 2015, Page(s):1659 - 1668
Cited by:  Papers (7)
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Single-layer feedforward networks (SLFNs) have been proven to be a universal approximator when all the parameters are allowed to be adjustable. It is widely used in classification and regression problems. The SLFN learning involves two tasks: determining network size and training the parameters. Most current algorithms could not be satisfactory to both sides. Some algorithms focused on constructio... View full abstract»

• ### Two-Stage Regularized Linear Discriminant Analysis for 2-D Data

Publication Year: 2015, Page(s):1669 - 1681
Cited by:  Papers (1)
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Fisher linear discriminant analysis (LDA) involves within-class and between-class covariance matrices. For 2-D data such as images, regularized LDA (RLDA) can improve LDA due to the regularized eigenvalues of the estimated within-class matrix. However, it fails to consider the eigenvectors and the estimated between-class matrix. To improve these two matrices simultaneously, we propose in this pape... View full abstract»

• ### Region-Based Object Recognition by Color Segmentation Using a Simplified PCNN

Publication Year: 2015, Page(s):1682 - 1697
Cited by:  Papers (8)
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In this paper, we propose a region-based object recognition (RBOR) method to identify objects from complex real-world scenes. First, the proposed method performs color image segmentation by a simplified pulse-coupled neural network (SPCNN) for the object model image and test image, and then conducts a region-based matching between them. Hence, we name it as RBOR with SPCNN (SPCNN-RBOR). Hereinto, ... View full abstract»

• ### Graph Theory-Based Approach for Stability Analysis of Stochastic Coupled Systems With Lévy Noise on Networks

Publication Year: 2015, Page(s):1698 - 1709
Cited by:  Papers (20)
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In this paper, a novel class of stochastic coupled systems with Lévy noise on networks (SCSLNNs) is presented. Both white noise and Lévy noise are considered in the networks. By exploiting graph theory and Lyapunov stability theory, criteria ensuring pth moment exponential stability and stability in probability of these SCSLNNs are established, respectively. These principles are cl... View full abstract»

• ### Asynchronous Event-Based Multikernel Algorithm for High-Speed Visual Features Tracking

Publication Year: 2015, Page(s):1710 - 1720
Cited by:  Papers (15)
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This paper presents a number of new methods for visual tracking using the output of an event-based asynchronous neuromorphic dynamic vision sensor. It allows the tracking of multiple visual features in real time, achieving an update rate of several hundred kilohertz on a standard desktop PC. The approach has been specially adapted to take advantage of the event-driven properties of these sensors b... View full abstract»

• ### An Experimentation Platform for On-Chip Integration of Analog Neural Networks: A Pathway to Trusted and Robust Analog/RF ICs

Publication Year: 2015, Page(s):1721 - 1734
Cited by:  Papers (4)
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We discuss the design of an experimentation platform intended for prototyping low-cost analog neural networks for on-chip integration with analog/RF circuits. The objective of such integration is to support various tasks, such as self-test, self-tuning, and trust/aging monitoring, which require classification of analog measurements obtained from on-chip sensors. Particular emphasis is given to cos... View full abstract»

• ### Opportunistic Behavior in Motivated Learning Agents

Publication Year: 2015, Page(s):1735 - 1746
Cited by:  Papers (7)
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This paper focuses on the novel motivated learning (ML) scheme and opportunistic behavior of an intelligent agent. It extends previously developed ML to opportunistic behavior in a multitask situation. Our paper describes the virtual world implementation of autonomous opportunistic agents learning in a dynamically changing environment, creating abstract goals, and taking advantage of arising oppor... View full abstract»

• ### Adaptive Batch Mode Active Learning

Publication Year: 2015, Page(s):1747 - 1760
Cited by:  Papers (3)
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Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar and representative instances to be selected for manual annotation. More recently, there have been attempts toward a batch mode form of active learning, where a batch of dat... View full abstract»

• ### Incremental Generalized Discriminative Common Vectors for Image Classification

Publication Year: 2015, Page(s):1761 - 1775
Cited by:  Papers (2)
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Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incr... View full abstract»

• ### Finite-Horizon Near-Optimal Output Feedback Neural Network Control of Quantized Nonlinear Discrete-Time Systems With Input Constraint

Publication Year: 2015, Page(s):1776 - 1788
Cited by:  Papers (4)
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The output feedback-based near-optimal regulation of uncertain and quantized nonlinear discrete-time systems in affine form with control constraint over finite horizon is addressed in this paper. First, the effect of input constraint is handled using a nonquadratic cost functional. Next, a neural network (NN)-based Luenberger observer is proposed to reconstruct both the system states and the contr... View full abstract»

• ### Adaptive Neural Output Feedback Control of Output-Constrained Nonlinear Systems With Unknown Output Nonlinearity

Publication Year: 2015, Page(s):1789 - 1802
Cited by:  Papers (20)
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This paper addresses the problem of adaptive neural output-feedback control for a class of special nonlinear systems with the hysteretic output mechanism and the unmeasured states. A modified Bouc-Wen model is first employed to capture the output hysteresis phenomenon in the design procedure. For its fusion with the neural networks and the Nussbaum-type function, two key lemmas are established usi... View full abstract»

• ### Missile Guidance Law Based on Robust Model Predictive Control Using Neural-Network Optimization

Publication Year: 2015, Page(s):1803 - 1809
Cited by:  Papers (9)
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In this brief, the utilization of robust model-based predictive control is investigated for the problem of missile interception. Treating the target acceleration as a bounded disturbance, novel guidance law using model predictive control is developed by incorporating missile inside constraints. The combined model predictive approach could be transformed as a constrained quadratic programming (QP) ... View full abstract»

• ### Constructing Optimal Prediction Intervals by Using Neural Networks and Bootstrap Method

Publication Year: 2015, Page(s):1810 - 1815
Cited by:  Papers (3)
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This brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estimation of the target variance in the bootstrap method. An optimization algorithm is developed for minimization of the cost function and adjustment of NN... View full abstract»

• ### Robust Exemplar Extraction Using Structured Sparse Coding

Publication Year: 2015, Page(s):1816 - 1821
Cited by:  Papers (45)
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Robust exemplar extraction from the noisy sample set is one of the most important problems in pattern recognition. In this brief, we propose a novel approach for exemplar extraction through structured sparse learning. The new model accounts for not only the reconstruction capability and the sparsity, but also the diversity and robustness. To solve the optimization problem, we adopt the alternating... View full abstract»

• ### Adaptive Neural Control of Nonaffine Systems With Unknown Control Coefficient and Nonsmooth Actuator Nonlinearities

Publication Year: 2015, Page(s):1822 - 1827
Cited by:  Papers (3)
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This brief considers the asymptotic tracking problem for a class of high-order nonaffine nonlinear dynamical systems with nonsmooth actuator nonlinearities. A novel transformation approach is proposed, which is able to systematically transfer the original nonaffine nonlinear system into an equivalent affine one. Then, to deal with the unknown dynamics and unknown control coefficient contained in t... View full abstract»

• ### Comparison of $ell _{1}$ -Norm SVR and Sparse Coding Algorithms for Linear Regression

Publication Year: 2015, Page(s):1828 - 1833
Cited by:  Papers (5)
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Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR an... View full abstract»

• ### Model-Free Dual Heuristic Dynamic Programming

Publication Year: 2015, Page(s):1834 - 1839
Cited by:  Papers (27)
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Model-based dual heuristic dynamic programming (MB-DHP) is a popular approach in approximating optimal solutions in control problems. Yet, it usually requires offline training for the model network, and thus resulting in extra computational cost. In this brief, we propose a model-free DHP (MF-DHP) design based on finite-difference technique. In particular, we adopt multilayer perceptron with one h... View full abstract»

## Aims & Scope

IEEE Transactions on Neural Networks and Learning Systems publishes technical articles that deal with the theory, design, and applications of neural networks and related learning systems.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Haibo He
Dept. of Electrical, Computer, and Biomedical Engineering
University of Rhode Island
Kingston, RI 02881, USA
ieeetnnls@gmail.com