# IEEE Transactions on Neural Networks and Learning Systems

## Issue 1 • Jan. 2019

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## Filter Results

Displaying Results 1 - 25 of 31

Publication Year: 2019, Page(s):C1 - 1
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• ### IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Publication Year: 2019, Page(s): C2
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• ### Editorial: Booming of Neural Networks and Learning Systems

Publication Year: 2019, Page(s):2 - 10
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• ### Deep CNN-Based Blind Image Quality Predictor

Publication Year: 2019, Page(s):11 - 24
Cited by:  Papers (2)
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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»

• ### Neuro-Adaptive Control With Given Performance Specifications for Strict Feedback Systems Under Full-State Constraints

Publication Year: 2019, Page(s):25 - 34
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In this paper, we investigate the tracking control problem for a class of strict feedback systems with pregiven performance specifications as well as full-state constraints. Our focus is on developing a feasible neural network (NN)-based control method that is able to, under full-state constraints, force the tracking error to converge into a prescribed region within preset finite time and further ... View full abstract»

• ### Consensus Problems Over Cooperation-Competition Random Switching Networks With Noisy Channels

Publication Year: 2019, Page(s):35 - 43
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In this paper, distributed iterative algorithms for consensus problems are considered for multiagent networks. Each agent randomly contacts with other agents at each instant and receives corrupted information due to the noisy channel from its neighborhood. Neighbors of each agent are cooperative or competitive, i.e., the elements in the adjacent weight matrix may be positive or negative. In such a... View full abstract»

• ### Estimation of Graphlet Counts in Massive Networks

Publication Year: 2019, Page(s):44 - 57
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Graphlets are induced subgraphs of a large network and are important for understanding and modeling complex networks. Despite their practical importance, graphlets have been severely limited to applications and domains with relatively small graphs. Most previous work has focused onexact algorithms; however, it is often too expensive to compute graphlets exactly in massive networks... View full abstract»

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

Publication Year: 2019, Page(s):58 - 71
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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»

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

Publication Year: 2019, Page(s):72 - 84
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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»

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

Publication Year: 2019, Page(s):85 - 96
Cited by:  Papers (1)
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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»

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

Publication Year: 2019, Page(s):97 - 108
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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»

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

Publication Year: 2019, Page(s):109 - 122
Cited by:  Papers (2)
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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: 2019, Page(s):123 - 137
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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»

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

Publication Year: 2019, Page(s):138 - 150
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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 Discrete-Time Projection Neural Network for Sparse Signal Reconstruction With Application to Face Recognition

Publication Year: 2019, Page(s):151 - 162
Cited by:  Papers (1)
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## 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