# IEEE Transactions on Neural Networks and Learning Systems

### Early Access Articles

Early Access articles are made available in advance of the final electronic or print versions. Early Access articles are peer reviewed but may not be fully edited. They are fully citable from the moment they appear in IEEE Xplore.

## Filter Results

Displaying Results 1 - 25 of 205
• ### Adaptive Neural Network Tracking Control for Robotic Manipulators With Dead Zone

Publication Year: 2018, Page(s):1 - 10
| | PDF (2969 KB)

In this paper, the adaptive neural network (NN) tracking control problem is addressed for robot manipulators subject to dead-zone input. The control objective is to design an adaptive NN controller to guarantee the stability of the systems and obtain good performance. Different from the existing results, which used NN to approximate the nonlinearities directly, NNs are employed to identify the ori... View full abstract»

• ### Adaptive Neural Control of a Kinematically Redundant Exoskeleton Robot Using Brain-Machine Interfaces

Publication Year: 2018, Page(s):1 - 14
| | PDF (4286 KB)

In this paper, a closed-loop control has been developed for the exoskeleton robot system based on brain-machine interface (BMI). Adaptive controllers in joint space, a redundancy resolution method at the velocity level, and commands that generated from BMI in task space have been integrated effectively to make the robot perform manipulation tasks controlled by human operator's electroencephalogram... View full abstract»

• ### A Nonconvex Relaxation Approach to Low-Rank Tensor Completion

Publication Year: 2018, Page(s):1 - 13
| | PDF (3213 KB)

Low-rank tensor completion plays an important role in many applications such as image processing, computer vision, and machine learning. A widely used convex relaxation of this problem is to minimize the nuclear norm of the square deal matrix generated by reshaping a tensor. However, this approach can be substantially suboptimal. In order to seek a highly accurate solution, in this paper, we propo... View full abstract»

• ### A Local and Global Discriminative Framework and Optimization for Balanced Clustering

Publication Year: 2018, Page(s):1 - 13
| | PDF (2375 KB)

For many specific applications in data mining and machine learning, we face explicit or latent size constraint for each cluster that leads to the balanced clustering'' problem. Many existing clustering algorithms perform well in partitioning but fail in producing balanced clusters and preserving the naturally balanced structure of some data. In this paper, we propose a novel balanced clustering ... View full abstract»

• ### Event-Sampled Output Feedback Control of Robot Manipulators Using Neural Networks

Publication Year: 2018, Page(s):1 - 8
| | PDF (1989 KB)

In this paper, adaptive neural networks (NNs) are employed in the event-triggered feedback control framework to enable a robot manipulator to track a predefined trajectory. In the proposed output feedback control scheme, the joint velocities of the robot manipulator are reconstructed using a nonlinear NN observer by using the joint position measurements. Two different configurations are proposed f... View full abstract»

• ### A Deep Learning Approach to Competing Risks Representation in Peer-to-Peer Lending

Publication Year: 2018, Page(s):1 - 10
| | PDF (1775 KB)

Online peer-to-peer (P2P) lending is expected to benefit both investors and borrowers due to their low transaction cost and the elimination of expensive intermediaries. From the lenders' perspective, maximizing their return on investment is an ultimate goal during their decision-making procedure. In this paper, we explore and address a fundamental problem underlying such a goal: how to represent t... View full abstract»

• ### Exploiting Generalization in the Subspaces for Faster Model-Based Reinforcement Learning

Publication Year: 2018, Page(s):1 - 16
| | PDF (3855 KB) |  Media

Due to the lack of enough generalization in the state space, common methods of reinforcement learning suffer from slow learning speed, especially in the early learning trials. This paper introduces a model-based method in discrete state spaces for increasing the learning speed in terms of required experiences (but not required computation time) by exploiting generalization in the experiences of th... View full abstract»

• ### Spatio-Temporal Interpolated Echo State Network for Meteorological Series Prediction

Publication Year: 2018, Page(s):1 - 14
| | PDF (3635 KB)

Spatio-temporal series prediction has attracted increasing attention in the field of meteorology in recent years. The spatial and temporal joint effect makes predictions challenging. Most of the existing spatio-temporal prediction models are computationally complicated. To develop an accurate but easy-to-implement spatio-temporal prediction model, this paper designs a novel spatio-temporal predict... View full abstract»

• ### STRAINet: Spatially Varying sTochastic Residual AdversarIal Networks for MRI Pelvic Organ Segmentation

Publication Year: 2018, Page(s):1 - 13
| | PDF (2486 KB)

Accurate segmentation of pelvic organs is important for prostate radiation therapy. Modern radiation therapy starts to use a magnetic resonance image (MRI) as an alternative to computed tomography image because of its superior soft tissue contrast and also free of risk from radiation exposure. However, segmentation of pelvic organs from MRI is a challenging problem due to inconsistent organ appear... View full abstract»

• ### A Robust AUC Maximization Framework With Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification

Publication Year: 2018, Page(s):1 - 12
| | PDF (2153 KB)

The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as positive'' together with a large volume of unlabeled'' samples that may contain both positive and negative samples. Building robust classifiers for the PU problem is very challenging, especially... View full abstract»

• ### Approximate Optimal Distributed Control of Nonlinear Interconnected Systems Using Event-Triggered Nonzero-Sum Games

Publication Year: 2018, Page(s):1 - 11
| | PDF (1386 KB)

In this paper, approximate optimal distributed control schemes for a class of nonlinear interconnected systems with strong interconnections are presented using continuous and event-sampled feedback information. The optimal control design is formulated as an $N$-player nonzero-sum game where the control policies of the subsystems act as players. An approximate Nash equilibrium solution to the game,... View full abstract»

• ### Output Feedback Q-Learning Control for the Discrete-Time Linear Quadratic Regulator Problem

Publication Year: 2018, Page(s):1 - 14
| | PDF (2532 KB)

Approximate dynamic programming (ADP) and reinforcement learning (RL) have emerged as important tools in the design of optimal and adaptive control systems. Most of the existing RL and ADP methods make use of full-state feedback, a requirement that is often difficult to satisfy in practical applications. As a result, output feedback methods are more desirable as they relax this requirement. In thi... View full abstract»

• ### Multistability of Delayed Hybrid Impulsive Neural Networks With Application to Associative Memories

Publication Year: 2018, Page(s):1 - 15
| | PDF (2257 KB)

The important topic of multistability of continuous- and discrete-time neural network (NN) models has been investigated rather extensively. Concerning the design of associative memories, multistability of delayed hybrid NNs is studied in this paper with an emphasis on the impulse effects. Arising from the spiking phenomenon in biological networks, impulsive NNs provide an efficient model for synap... View full abstract»

• ### Early Expression Detection via Online Multi-Instance Learning With Nonlinear Extension

Publication Year: 2018, Page(s):1 - 11
| | PDF (2248 KB)

Video-based facial expression recognition has received substantial attention over the past decade, while early expression detection (EED) is still a relatively new and challenging problem. The goal of EED is to identify an expression as quickly as possible after the expression starts and before it ends. This timely ability has many potential applications, ranging from human-computer interaction to... View full abstract»

• ### LTNN: A Layerwise Tensorized Compression of Multilayer Neural Network

Publication Year: 2018, Page(s):1 - 15
| | PDF (3838 KB)

An efficient deep learning requires a memory-efficient construction of a neural network. This paper introduces a layerwise tensorized formulation of a multilayer neural network, called LTNN, such that the weight matrix can be significantly compressed during training. By reshaping the multilayer neural network weight matrix into a high-dimensional tensor with a low-rank approximation, significant n... View full abstract»

• ### Novel Finite-Time Synchronization Criteria for Inertial Neural Networks With Time Delays via Integral Inequality Method

Publication Year: 2018, Page(s):1 - 10
| | PDF (1305 KB)

In this paper, we are concerned with the finite-time synchronization of a class of inertial neural networks with time delays. Without applying some finite-time stability theorems, which are widely applied to studying the finite-time synchronization for neural networks, by constructing two Lyapunov functions and using integral inequality method, two sufficient conditions on the finite-time synchron... View full abstract»

• ### Hypergraph-Induced Convolutional Networks for Visual Classification

Publication Year: 2018, Page(s):1 - 10
| | PDF (3682 KB)

At present, convolutional neural networks (CNNs) have become popular in visual classification tasks because of their superior performance. However, CNN-based methods do not consider the correlation of visual data to be classified. Recently, graph convolutional networks (GCNs) have mitigated this problem by modeling the pairwise relationship in visual data. Real-world tasks of visual classification... View full abstract»

• ### Output-Feedback Adaptive Neural Controller for Uncertain Pure-Feedback Nonlinear Systems Using a High-Order Sliding Mode Observer

Publication Year: 2018, Page(s):1 - 6
| | PDF (566 KB)

A novel adaptive neural output-feedback controller for SISO nonaffine pure-feedback nonlinear systems is proposed. The majority of the previously described adaptive neural controllers for pure-feedback nonlinear systems were based on the dynamic surface control (DSC) or backstepping schemes. This makes the control law as well as the stability analysis highly lengthy and complicated. Moreover, ther... View full abstract»

• ### Distributed Adaptive Tracking Synchronization for Coupled Reaction-Diffusion Neural Network

Publication Year: 2018, Page(s):1 - 14
| | PDF (1810 KB)

This paper considers the tracking synchronization problem for a class of coupled reaction-diffusion neural networks (CRDNNs) with undirected topology. For the case where the tracking trajectory has identical individual dynamic as that of the network nodes, the edge-based and vertex-based adaptive strategies on coupling strengths as well as adaptive controllers, which demand merely the local neighb... View full abstract»

• ### Face Sketch Synthesis by Multidomain Adversarial Learning

Publication Year: 2018, Page(s):1 - 10
| | PDF (6053 KB)

Given a training set of face photo-sketch pairs, face sketch synthesis targets at learning a mapping from the photo domain to the sketch domain. Despite the exciting progresses made in the literature, it retains as an open problem to synthesize high-quality sketches against blurs and deformations. Recent advances in generative adversarial training provide a new insight into face sketch synthesis, ... View full abstract»

• ### Nonuniformly Sampled Data Processing Using LSTM Networks

Publication Year: 2018, Page(s):1 - 10
| | PDF (1766 KB)

We investigate classification and regression for nonuniformly sampled variable length sequential data and introduce a novel long short-term memory (LSTM) architecture. In particular, we extend the classical LSTM network with additional time gates, which incorporate the time information as a nonlinear scaling factor on the conventional gates. We also provide forward-pass and backward-pass update eq... View full abstract»

• ### Deep Semantic-Preserving Ordinal Hashing for Cross-Modal Similarity Search

Publication Year: 2018, Page(s):1 - 12
| | PDF (2063 KB)

Cross-modal hashing has attracted increasing research attention due to its efficiency for large-scale multimedia retrieval. With simultaneous feature representation and hash function learning, deep cross-modal hashing (DCMH) methods have shown superior performance. However, most existing methods on DCMH adopt binary quantization functions (e.g., sign(·)) to generate hash codes, which limit the ret... View full abstract»

• ### Multiobjective Support Vector Machines: Handling Class Imbalance With Pareto Optimality

Publication Year: 2018, Page(s):1 - 7
| | PDF (1378 KB) |  Media

Support vector machines (SVMs) seek to optimize three distinct objectives: maximization of margin, minimization of regularization from the positive class, and minimization of regularization from the negative class. The right choice of weightage for each of these objectives is critical to the quality of the classifier learned, especially in case of the class imbalanced data sets. Therefore, costly ... View full abstract»

• ### Deep Neural Network Initialization With Decision Trees

Publication Year: 2018, Page(s):1 - 10
| | PDF (2273 KB)

In this paper, a novel, automated process for constructing and initializing deep feedforward neural networks based on decision trees is presented. The proposed algorithm maps a collection of decision trees trained on the data into a collection of initialized neural networks with the structures of the networks determined by the structures of the trees. The tree-informed initialization acts as a war... View full abstract»

• ### Bag-Level Aggregation for Multiple-Instance Active Learning in Instance Classification Problems

Publication Year: 2018, Page(s):1 - 11
| | PDF (1678 KB)

A growing number of applications, e.g., video surveillance and medical image analysis, require training recognition systems from large amounts of weakly annotated data, while some targeted interactions with a domain expert are allowed to improve the training process. In such cases, active learning (AL) can reduce labeling costs for training a classifier by querying the expert to provide the labels... 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