IEEE Transactions on Neural Networks and Learning Systems

Volume 28 Issue 7 • July 2017

The purchase and pricing options for this item are unavailable. Select items are only available as part of a subscription package. You may try again later or contact us for more information.

Filter Results

Displaying Results 1 - 25 of 28

Publication Year: 2017, Page(s):C1 - 1489
| PDF (115 KB)
• IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Publication Year: 2017, Page(s): C2
| PDF (68 KB)
• Feature Selection Based on Structured Sparsity: A Comprehensive Study

Publication Year: 2017, Page(s):1490 - 1507
Cited by:  Papers (36)
| | PDF (3835 KB) | HTML

Feature selection (FS) is an important component of many pattern recognition tasks. In these tasks, one is often confronted with very high-dimensional data. FS algorithms are designed to identify the relevant feature subset from the original features, which can facilitate subsequent analysis, such as clustering and classification. Structured sparsity-inducing feature selection (SSFS) methods have ... View full abstract»

• Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer’s Disease

Publication Year: 2017, Page(s):1508 - 1519
Cited by:  Papers (8)
| | PDF (1960 KB) | HTML

Understanding the progression of chronic diseases can empower the sufferers in taking proactive care. To predict the disease status in the future time points, various machine learning approaches have been proposed. However, a few of them jointly consider the dual heterogeneities of chronic disease progression. In particular, the predicting task at each time point has features from multiple sources... View full abstract»

• Observer-Based Adaptive NN Control for a Class of Uncertain Nonlinear Systems With Nonsymmetric Input Saturation

Publication Year: 2017, Page(s):1520 - 1530
Cited by:  Papers (7)
| | PDF (1514 KB) | HTML

This paper is concerned with the problem of adaptive tracking control for a class of uncertain nonlinear systems with nonsymmetric input saturation and immeasurable states. The radial basis function of neural network (NN) is employed to approximate unknown functions, and an NN state observer is designed to estimate the immeasurable states. To analyze the effect of input saturation, an auxiliary sy... View full abstract»

• Neural Approximation-Based Adaptive Control for a Class of Nonlinear Nonstrict Feedback Discrete-Time Systems

Publication Year: 2017, Page(s):1531 - 1541
Cited by:  Papers (22)
| | PDF (2675 KB) | HTML

In this paper, an adaptive control approach-based neural approximation is developed for a class of uncertain nonlinear discrete-time (DT) systems. The main characteristic of the considered systems is that they can be viewed as a class of multi-input multioutput systems in the nonstrict feedback structure. The similar control problem of this class of systems has been addressed in the past, but it f... View full abstract»

• Characterization of Linearly Separable Boolean Functions: A Graph-Theoretic Perspective

Publication Year: 2017, Page(s):1542 - 1549
Cited by:  Papers (1)
| | PDF (3454 KB) | HTML

In this paper, we present a novel approach for studying Boolean function in a graph-theoretic perspective. In particular, we first transform a Boolean function f of n variables into an induced subgraph Hf of the n-dimensional hypercube, and then, we show the properties of linearly separable Boolean functions on the basis of the analysis of the structure of Hf . We define a new class of graphs, cal... View full abstract»

• Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks

Publication Year: 2017, Page(s):1550 - 1559
Cited by:  Papers (13)
| | PDF (1901 KB) | HTML

This paper tries to separate fine-grained images by jointly learning the encoding parameters and codebooks through low-rank sparse coding (LRSC) with general and class-specific codebook generation. Instead of treating each local feature independently, we encode the local features within a spatial region jointly by LRSC. This ensures that the spatially nearby local features with similar visual char... View full abstract»

• Impulsive Multisynchronization of Coupled Multistable Neural Networks With Time-Varying Delay

Publication Year: 2017, Page(s):1560 - 1571
Cited by:  Papers (26)
| | PDF (26216 KB) | HTML

This paper studies the synchronization problem of coupled delayed multistable neural networks (NNs) with directed topology. To begin with, several sufficient conditions are developed in terms of algebraic inequalities such that every subnetwork has multiple locally exponentially stable periodic orbits or equilibrium points. Then two new concepts named dynamical multisynchronization (DMS) and stati... View full abstract»

• Embedded Streaming Deep Neural Networks Accelerator With Applications

Publication Year: 2017, Page(s):1572 - 1583
Cited by:  Papers (10)
| | PDF (2003 KB) | HTML

Deep convolutional neural networks (DCNNs) have become a very powerful tool in visual perception. DCNNs have applications in autonomous robots, security systems, mobile phones, and automobiles, where high throughput of the feedforward evaluation phase and power efficiency are important. Because of this increased usage, many field-programmable gate array (FPGA)-based accelerators have been proposed... View full abstract»

• Solution Path for Pin-SVM Classifiers With Positive and Negative $\tau$ Values

Publication Year: 2017, Page(s):1584 - 1593
Cited by:  Papers (6)
| | PDF (1531 KB) | HTML

Applying the pinball loss in a support vector machine (SVM) classifier results in pin-SVM. The pinball loss is characterized by a parameter τ. Its value is related to the quantile level and different τ values are suitable for different problems. In this paper, we establish an algorithm to find the entire solution path for pin-SVM with different τ values. This algorithm is based on the fact that th... View full abstract»

• Adaptive Event-Triggered Control Based on Heuristic Dynamic Programming for Nonlinear Discrete-Time Systems

Publication Year: 2017, Page(s):1594 - 1605
Cited by:  Papers (16)
| | PDF (2459 KB) | HTML

This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) ... View full abstract»

• An Alternating Identification Algorithm for a Class of Nonlinear Dynamical Systems

Publication Year: 2017, Page(s):1606 - 1617
Cited by:  Papers (5)
| | PDF (2075 KB) | HTML

While modeling nonlinear systems by combining a linear model with a nonlinear compensation term, namely, virtual unmodeled dynamics (VUD), the parameter estimation of the linear model and the learning-based VUD estimate influences and interacts with each other simultaneously. This paper aims to develop an alternating identification scheme for resolving such a challenging problem, where a projectio... View full abstract»

• Exponential Synchronization for Markovian Stochastic Coupled Neural Networks of Neutral-Type via Adaptive Feedback Control

Publication Year: 2017, Page(s):1618 - 1632
Cited by:  Papers (18)
| | PDF (2088 KB) | HTML

In this paper, we investigate the adaptive exponential synchronization in both the mean square and the almost sure senses for an array of N identical Markovian stochastic coupled neural networks of neutral-type with time-varying delay and random coupling strength. The generalized Lyapunov theorem of the exponential stability in the mean square for the neutral stochastic Markov system with the time... View full abstract»

• Observer-Based Adaptive Neural Network Trajectory Tracking Control for Remotely Operated Vehicle

Publication Year: 2017, Page(s):1633 - 1645
Cited by:  Papers (39)
| | PDF (2971 KB) | HTML

This paper focuses on the adaptive trajectory tracking control for a remotely operated vehicle (ROV) with an unknown dynamic model and the unmeasured states. Unlike most previous trajectory tracking control approaches, in this paper, the velocity states and the angular velocity states in the body-fixed frame are unmeasured, and the thrust model is inaccurate. Obviously, it is more in line with the... View full abstract»

• Structural Minimax Probability Machine

Publication Year: 2017, Page(s):1646 - 1656
Cited by:  Papers (191)
| | PDF (1786 KB) | HTML

Minimax probability machine (MPM) is an interesting discriminative classifier based on generative prior knowledge. It can directly estimate the probabilistic accuracy bound by minimizing the maximum probability of misclassification. The structural information of data is an effective way to represent prior knowledge, and has been found to be vital for designing classifiers in real-world problems. H... View full abstract»

• Global Synchronization of Multiple Recurrent Neural Networks With Time Delays via Impulsive Interactions

Publication Year: 2017, Page(s):1657 - 1667
Cited by:  Papers (11)
| | PDF (2354 KB) | HTML

In this paper, new results on the global synchronization of multiple recurrent neural networks (NNs) with time delays via impulsive interactions are presented. Impulsive interaction means that a number of NNs communicate with each other at impulse instants only, while they are independent at the remaining time. The communication topology among NNs is not required to be always connected and can swi... View full abstract»

• Batch Mode Active Learning for Regression With Expected Model Change

Publication Year: 2017, Page(s):1668 - 1681
Cited by:  Papers (4)
| | PDF (2707 KB) | HTML

While active learning (AL) has been widely studied for classification problems, limited efforts have been done on AL for regression. In this paper, we introduce a new AL framework for regression, expected model change maximization (EMCM), which aims at choosing the unlabeled data instances that result in the maximum change of the current model once labeled. The model change is quantified as the di... View full abstract»

• Prediction Reweighting for Domain Adaptation

Publication Year: 2017, Page(s):1682 - 1695
Cited by:  Papers (3)
| | PDF (3222 KB) | HTML

There are plenty of classification methods that perform well when training and testing data are drawn from the same distribution. However, in real applications, this condition may be violated, which causes degradation of classification accuracy. Domain adaptation is an effective approach to address this problem. In this paper, we propose a general domain adaptation framework from the perspective o... View full abstract»

• Switched-Observer-Based Adaptive Neural Control of MIMO Switched Nonlinear Systems With Unknown Control Gains

Publication Year: 2017, Page(s):1696 - 1709
Cited by:  Papers (7)
| | PDF (1578 KB) | HTML

In this paper, the problem of adaptive neural output-feedback control is addressed for a class of multi-input multioutput (MIMO) switched uncertain nonlinear systems with unknown control gains. Neural networks (NNs) are used to approximate unknown nonlinear functions. In order to avoid the conservativeness caused by adoption of a common observer for all subsystems, an MIMO NN switched observer is ... View full abstract»

• $\mu$ -Stability of Nonlinear Positive Systems With Unbounded Time-Varying Delays

Publication Year: 2017, Page(s):1710 - 1715
Cited by:  Papers (6)
| | PDF (289 KB) | HTML

The stability of the zero solution plays an important role in the investigation of positive systems. In this brief, we discuss the μ-stability of positive nonlinear systems with unbounded time-varying delays. The system is modeled by the continuous-time ordinary differential equation. Under some assumptions on the nonlinear functions, such as homogeneous, cooperative, and nondecreasing, we propose... View full abstract»

• Learning With Auxiliary Less-Noisy Labels

Publication Year: 2017, Page(s):1716 - 1721
Cited by:  Papers (3)
| | PDF (1013 KB) | HTML

Obtaining a sufficient number of accurate labels to form a training set for learning a classifier can be difficult due to the limited access to reliable label resources. Instead, in real-world applications, less-accurate labels, such as labels from nonexpert labelers, are often used. However, learning with less-accurate labels can lead to serious performance deterioration because of the high noise... View full abstract»

• Import Vector Domain Description: A Kernel Logistic One-Class Learning Algorithm

Publication Year: 2017, Page(s):1722 - 1729
Cited by:  Papers (1)
| | PDF (1312 KB) | HTML

Recognizing the samples belonging to one class in a heterogeneous data set is a very interesting but tough machine learning task. Some samples of the data set can be actual outliers or members of other classes for which training examples are lacking. In contrast to other kernel approaches present in the literature, in this work, the problem is faced defining a one-class kernel machine that deliver... View full abstract»

• Call For Papers: IEEE World Congress on Computational Intelligence

Publication Year: 2017, Page(s): 1730
| PDF (1319 KB)
• 2018 IEEE Symposiuim Series on Computational Intelligence

Publication Year: 2017, Page(s): 1731
| PDF (2422 KB)

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