# IEEE Transactions on Neural Networks and Learning Systems

## Issue 99

Early Access articles are new content made available in advance of the final electronic or print versions and result from IEEE's Preprint or Rapid Post processes. Preprint articles are peer-reviewed but not fully edited. Rapid Post articles are peer-reviewed and edited but not paginated. Both these types of Early Access articles are fully citable from the moment they appear in IEEE Xplore.

## Filter Results

Displaying Results 1 - 25 of 343
• ### A Novel Neural Networks Ensemble Approach for Modeling Electrochemical Cells

Publication Year: 2018, Page(s):1 - 12
| | PDF (3389 KB)

Accurate modeling of electrochemical cells is nowadays mandatory for achieving effective upgrades in the fields of energetic efficiency and sustainable mobility. Indeed, these models are often used for performing accurate State-of-Charge (SoC) estimations in energy storage systems used in microgrids or powering pure electric and hybrid cars. To this aim, a novel neural networks ensemble approach f... View full abstract»

• ### Neurons With Paraboloid Decision Boundaries for Improved Neural Network Classification Performance

Publication Year: 2018, Page(s):1 - 11
| | PDF (1807 KB)

In mathematical terms, an artificial neuron computes the inner product of a d-dimensional input vector x with its weight vector w, compares it with a bias value w₀ and fires based on the result of this comparison. Therefore, its decision boundary is given by the equation wTx+w₀=0. In this paper, we propose replacing the linear hyperplane decision boundary of a neuron with a curved, p... View full abstract»

• ### A Semisupervised Classification Approach for Multidomain Networks With Domain Selection

Publication Year: 2018, Page(s):1 - 15
| | PDF (4515 KB)

Multidomain network classification has attracted significant attention in data integration and machine learning, which can enhance network classification or prediction performance by integrating information from different sources. Despite the previous success, existing multidomain network learning methods usually assume that different views are available for the same set of instances, and thus, th... View full abstract»

• ### Augmented Real-Valued Time-Delay Neural Network for Compensation of Distortions and Impairments in Wireless Transmitters

Publication Year: 2018, Page(s):1 - 13
| | PDF (3030 KB)

A digital predistorter, modeled by an augmented real-valued time-delay neural network (ARVTDNN), has been proposed and found suitable to mitigate the nonlinear distortions of the power amplifier (PA) along with modulator imperfections for a wideband direct-conversion transmitter. The input signal of the proposed ARVTDNN consists of Cartesian in-phase and quadrature phase (I/Q) components, as well ... View full abstract»

• ### UCFTS: A Unilateral Coupling Finite-Time Synchronization Scheme for Complex Networks

Publication Year: 2018, Page(s):1 - 14
| | PDF (1891 KB)

Improving universality and robustness of the control method is one of the most challenging problems in the field of complex networks (CNs) synchronization. In this paper, a special unilateral coupling finite-time synchronization (UCFTS) method for uncertain CNs is proposed for this challenging problem. Multiple influencing factors are considered, so that the proposed method can be applied to a var... View full abstract»

• ### Deep CNN-Based Blind Image Quality Predictor

Publication Year: 2018, Page(s):1 - 14
| | PDF (7026 KB)

Image recognition based on convolutional neural networks (CNNs) has recently been shown to deliver the state-of-the-art performance in various areas of computer vision and image processing. Nevertheless, applying a deep CNN to no-reference image quality assessment (NR-IQA) remains a challenging task due to critical obstacles, i.e., the lack of a training database. In this paper, we propose a CNN-b... View full abstract»

• ### Blind Denoising Autoencoder

Publication Year: 2018, Page(s):1 - 6
| | PDF (1334 KB)

The term blind denoising' refers to the fact that the basis used for denoising is learned from the noisy sample itself during denoising. Dictionary learning- and transform learning-based formulations for blind denoising are well known. But there has been no autoencoder-based solution for the said blind denoising approach. So far, autoencoder-based denoising formulations have learned the model on... View full abstract»

• ### Variational Random Function Model for Network Modeling

Publication Year: 2018, Page(s):1 - 7
| | PDF (611 KB)

Link prediction is a fundamental problem in network modeling. A family of link prediction approaches is to treat network data as an exchangeable array whose entries can be explained by random functions (e.g., block models and Gaussian processes) over latent node factors. Despite their powerful ability in modeling missing links, these models tend to have a large computational complexity and thus ar... View full abstract»

• ### Semisupervised Learning Based on a Novel Iterative Optimization Model for Saliency Detection

Publication Year: 2018, Page(s):1 - 17
| | PDF (3840 KB)

In this paper, we propose a novel iterative optimization model for bottom-up saliency detection. By exploring bottom-up saliency principles and semisupervised learning approaches, we design a high-performance saliency analysis method for wide ranging scenes. The proposed algorithm consists of two stages: 1) we develop a boundary homogeneity model to characterize the general position and the contou... View full abstract»

• ### Filippov Hindmarsh-Rose Neuronal Model With Threshold Policy Control

Publication Year: 2018, Page(s):1 - 6
| | PDF (11503 KB)

A Filippov system of Hindmarsh-Rose (HR) neuronal model with threshold policy control is proposed, membrane potential has been taken as the threshold and the corresponding switching function is also established. We first discuss the existence and stability of the equilibria for the two Filippov subsystems based on the 2-D HR model. Subsequently, the sliding dynamics of HR model including the slidi... View full abstract»

• ### Leader-Following Practical Cluster Synchronization for Networks of Generic Linear Systems: An Event-Based Approach

Publication Year: 2018, Page(s):1 - 10
| | PDF (2111 KB)

In network systems, a group of nodes may evolve into several subgroups and coordinate with each other in the same subgroup, i.e., reach cluster synchronization, to cope with the unanticipated situations. To this end, the leader-following practical cluster synchronization problem of networks of generic linear systems is studied in this paper. An event-based control algorithm that can largely reduce... View full abstract»

• ### Exploiting Combination Effect for Unsupervised Feature Selection by ℓ2,0 Norm

Publication Year: 2018, Page(s):1 - 14
| | PDF (2647 KB)

In learning applications, exploring the cluster structures of the high dimensional data is an important task. It requires projecting or visualizing the cluster structures into a low dimensional space. The challenges are: 1) how to perform the projection or visualization with less information loss and 2) how to preserve the interpretability of the original data. Recent methods address these challen... View full abstract»

• ### On the Duality Between Belief Networks and Feed-Forward Neural Networks

Publication Year: 2018, Page(s):1 - 11
| | PDF (2516 KB)

This paper addresses the duality between the deterministic feed-forward neural networks (FF-NNs) and linear Bayesian networks (LBNs), which are the generative stochastic models representing probability distributions over the visible data based on a linear function of a set of latent (hidden) variables. The maximum entropy principle is used to define a unique generative model corresponding to each ... View full abstract»

• ### Reconstructible Nonlinear Dimensionality Reduction via Joint Dictionary Learning

Publication Year: 2018, Page(s):1 - 15
| | PDF (10566 KB)

This paper presents a parametric low-dimensional (LD) representation learning method that allows to reconstruct high-dimensional (HD) input vectors in an unsupervised manner. Under the assumption that the HD data and its LD representation share the same or similar local sparse structure, the proposed method achieves reconstructible dimensionality reduction via jointly learning dictionaries in both... View full abstract»

• ### Domain-Weighted Majority Voting for Crowdsourcing

Publication Year: 2018, Page(s):1 - 12
| | PDF (1680 KB)

Crowdsourcing labeling systems provide an efficient way to generate multiple inaccurate labels for given observations. If the competence level or the reputation,' which can be explained as the probabilities of annotating the right label, for each crowdsourcing annotators is equal and biased to annotate the right label, majority voting (MV) is the optimal decision rule for merging the multiple la... View full abstract»

• ### A Discrete-Time Projection Neural Network for Sparse Signal Reconstruction With Application to Face Recognition

Publication Year: 2018, Page(s):1 - 12
| | PDF (2693 KB)

This paper deals with sparse signal reconstruction by designing a discrete-time projection neural network. Sparse signal reconstruction can be converted into an L₁-minimization problem, which can also be changed into the unconstrained basis pursuit denoising problem. To solve the L₁-minimization problem, an iterative algorithm is proposed based on the discrete-time projection neural ... View full abstract»

• ### Quantized Sampled-Data Control for Synchronization of Inertial Neural Networks With Heterogeneous Time-Varying Delays

Publication Year: 2018, Page(s):1 - 11
| | PDF (2596 KB)

This paper is concerned with the problem of synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) through quantized sampled-data control. The control scheme, which takes the communication limitations of quantization and variable sampling into account, is first employed for tackling the synchronization of INNs. A novel Lyapunov-Krasovskii functional (LKF... View full abstract»

• ### Enhanced Robot Speech Recognition Using Biomimetic Binaural Sound Source Localization

Publication Year: 2018, Page(s):1 - 13
| | PDF (1872 KB)

Inspired by the behavior of humans talking in noisy environments, we propose an embodied embedded cognition approach to improve automatic speech recognition (ASR) systems for robots in challenging environments, such as with ego noise, using binaural sound source localization (SSL). The approach is verified by measuring the impact of SSL with a humanoid robot head on the performance of an ASR syste... View full abstract»

• ### A Cost-Sensitive Deep Belief Network for Imbalanced Classification

Publication Year: 2018, Page(s):1 - 14
| | PDF (3262 KB) |  Media

Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassi... View full abstract»

• ### A Highly Effective and Robust Membrane Potential-Driven Supervised Learning Method for Spiking Neurons

Publication Year: 2018, Page(s):1 - 15
| | PDF (3929 KB)

Spiking neurons are becoming increasingly popular owing to their biological plausibility and promising computational properties. Unlike traditional rate-based neural models, spiking neurons encode information in the temporal patterns of the transmitted spike trains, which makes them more suitable for processing spatiotemporal information. One of the fundamental computations of spiking neurons is t... View full abstract»

• ### Design and Adaptive Control for an Upper Limb Robotic Exoskeleton in Presence of Input Saturation

Publication Year: 2018, Page(s):1 - 12
| | PDF (2892 KB)

This paper addresses the control design for an upper limb exoskeleton in the presence of input saturation. An adaptive controller employing the neural network technology is proposed to approximate the uncertain robotic dynamics. Also, an auxiliary system is designed to deal with the effect of input saturation. Furthermore, we develop both the state feedback and the output feedback control strategi... View full abstract»

• ### Optimal Synchronization Control of Multiagent Systems With Input Saturation via Off-Policy Reinforcement Learning

Publication Year: 2018, Page(s):1 - 12
| | PDF (5209 KB)

In this paper, we aim to investigate the optimal synchronization problem for a group of generic linear systems with input saturation. To seek the optimal controller, Hamilton-Jacobi-Bellman (HJB) equations involving nonquadratic input energy terms in coupled forms are established. The solutions to these coupled HJB equations are further proven to be optimal and the induced controllers constitute i... View full abstract»

• ### Multiple-Model Adaptive Estimation for 3-D and 4-D Signals: A Widely Linear Quaternion Approach

Publication Year: 2018, Page(s):1 - 13
| | PDF (2025 KB)

Quaternion state estimation techniques have been used in various applications, yet they are only suitable for dynamical systems represented by a single known model. In order to deal with model uncertainty, this paper proposes a class of widely linear quaternion multiple-model adaptive estimation (WL-QMMAE) algorithms based on widely linear quaternion Kalman filters and Bayesian inference. The augm... View full abstract»

• ### Finite-Time Passivity-Based Stability Criteria for Delayed Discrete-Time Neural Networks via New Weighted Summation Inequalities

Publication Year: 2018, Page(s):1 - 14
| | PDF (1270 KB)

In this paper, we study the problem of finite-time stability and passivity criteria for discrete-time neural networks (DNNs) with variable delays. The main objective is how to effectively evaluate the finite-time passivity conditions for NNs. To achieve this, some new weighted summation inequalities are proposed for application to a finite-sum term appearing in the forward difference of a novel Ly... View full abstract»

• ### Adaptive Asymptotic Neural Network Control of Nonlinear Systems With Unknown Actuator Quantization

Publication Year: 2018, Page(s):1 - 10
| | PDF (3306 KB)

In this paper, we propose an adaptive neural-network-based asymptotic control algorithm for a class of nonlinear systems subject to unknown actuator quantization. To this end, we exploit the sector property of the quantization nonlinearity and transform actuator quantization control problem into analyzing its upper bounds, which are then handled by a dynamic loop gain function-based approach. In o... 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