# IEEE Transactions on Neural Networks and Learning Systems

## Filter Results

Displaying Results 1 - 25 of 39

Publication Year: 2015, Page(s):C1 - 2985
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• ### IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Publication Year: 2015, Page(s): C2
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• ### Farewell Editorial: Smooth Transition of IEEE TNNLS

Publication Year: 2015, Page(s): 2986
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• ### $H_{\infty }$State Estimation for Discrete-Time Delayed Systems of the Neural Network Type With Multiple Missing Measurements

Publication Year: 2015, Page(s):2987 - 2998
Cited by:  Papers (18)
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This paper investigates the H state estimation problem for a class of discrete-time nonlinear systems of the neural network type with random time-varying delays and multiple missing measurements. These nonlinear systems include recurrent neural networks, complex network systems, Lur'e systems, and so on which can be described by a unified model consisting of a linear dynamic system and a static no... View full abstract»

• ### Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses

Publication Year: 2015, Page(s):2999 - 3008
Cited by:  Papers (9)
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We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator compose... View full abstract»

• ### Robust Blind Learning Algorithm for Nonlinear Equalization Using Input Decision Information

Publication Year: 2015, Page(s):3009 - 3020
Cited by:  Papers (2)
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In this paper, we propose a new blind learning algorithm, namely, the Benveniste-Goursat input-output decision (BG-IOD), to enhance the convergence performance of neural network-based equalizers for nonlinear channel equalization. In contrast to conventional blind learning algorithms, where only the output of the equalizer is employed for updating system parameters, the BG-IOD exploits a new type ... View full abstract»

• ### A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis

Publication Year: 2015, Page(s):3021 - 3033
Cited by:  Papers (7)
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This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a cont... View full abstract»

• ### Bidirectional Active Learning: A Two-Way Exploration Into Unlabeled and Labeled Data Set

Publication Year: 2015, Page(s):3034 - 3044
Cited by:  Papers (16)
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In practical machine learning applications, human instruction is indispensable for model construction. To utilize the precious labeling effort effectively, active learning queries the user with selective sampling in an interactive way. Traditional active learning techniques merely focus on the unlabeled data set under a unidirectional exploration framework and suffer from model deterioration in th... View full abstract»

• ### An Asynchronous Neuromorphic Event-Driven Visual Part-Based Shape Tracking

Publication Year: 2015, Page(s):3045 - 3059
Cited by:  Papers (7)
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Object tracking is an important step in many artificial vision tasks. The current state-of-the-art implementations remain too computationally demanding for the problem to be solved in real time with high dynamics. This paper presents a novel real-time method for visual part-based tracking of complex objects from the output of an asynchronous event-based camera. This paper extends the pictorial str... View full abstract»

• ### Synchronization in Networks of Linearly Coupled Dynamical Systems via Event-Triggered Diffusions

Publication Year: 2015, Page(s):3060 - 3069
Cited by:  Papers (24)
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In this paper, we utilize event-triggered coupling configurations to realize synchronization of linearly coupled dynamical systems. Here, the diffusion couplings are set up from the latest observations of the nodes and their neighborhood and the next observation time is triggered by the proposed criteria based on the local neighborhood information as well. Two scenarios are considered: 1) continuo... View full abstract»

• ### RSTFC: A Novel Algorithm for Spatio-Temporal Filtering and Classification of Single-Trial EEG

Publication Year: 2015, Page(s):3070 - 3082
Cited by:  Papers (19)
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Learning optimal spatio-temporal filters is a key to feature extraction for single-trial electroencephalogram (EEG) classification. The challenges are controlling the complexity of the learning algorithm so as to alleviate the curse of dimensionality and attaining computational efficiency to facilitate online applications, e.g., brain-computer interfaces (BCIs). To tackle these barriers, this pape... View full abstract»

• ### Emotional Multiagent Reinforcement Learning in Spatial Social Dilemmas

Publication Year: 2015, Page(s):3083 - 3096
Cited by:  Papers (13)
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Social dilemmas have attracted extensive interest in the research of multiagent systems in order to study the emergence of cooperative behaviors among selfish agents. Understanding how agents can achieve cooperation in social dilemmas through learning from local experience is a critical problem that has motivated researchers for decades. This paper investigates the possibility of exploiting emotio... View full abstract»

• ### Peaking-Free Output-Feedback Adaptive Neural Control Under a Nonseparation Principle

Publication Year: 2015, Page(s):3097 - 3108
Cited by:  Papers (18)
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High-gain observers have been extensively applied to construct output-feedback adaptive neural control (ANC) for a class of feedback linearizable uncertain nonlinear systems under a nonlinear separation principle. Yet due to static-gain and linear properties, high-gain observers are usually subject to peaking responses and noise sensitivity. Existing adaptive neural network (NN) observers cannot e... View full abstract»

• ### Multistability for Delayed Neural Networks via Sequential Contracting

Publication Year: 2015, Page(s):3109 - 3122
Cited by:  Papers (11)
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In this paper, we explore a variety of new multistability scenarios in the general delayed neural network system. Geometric structure embedded in equations is exploited and incorporated into the analysis to elucidate the underlying dynamics. Criteria derived from different geometric configurations lead to disparate numbers of equilibria. A new approach named sequential contracting is applied to co... View full abstract»

• ### Competition and Collaboration in Cooperative Coevolution of Elman Recurrent Neural Networks for Time-Series Prediction

Publication Year: 2015, Page(s):3123 - 3136
Cited by:  Papers (36)
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Collaboration enables weak species to survive in an environment where different species compete for limited resources. Cooperative coevolution (CC) is a nature-inspired optimization method that divides a problem into subcomponents and evolves them while genetically isolating them. Problem decomposition is an important aspect in using CC for neuroevolution. CC employs different problem decompositio... View full abstract»

• ### DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons

Publication Year: 2015, Page(s):3137 - 3149
Cited by:  Papers (13)
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Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning me... View full abstract»

• ### Distance Metric Learning Using Privileged Information for Face Verification and Person Re-Identification

Publication Year: 2015, Page(s):3150 - 3162
Cited by:  Papers (20)
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In this paper, we propose a new approach to improve face verification and person re-identification in the RGB images by leveraging a set of RGB-D data, in which we have additional depth images in the training data captured using depth cameras such as Kinect. In particular, we extract visual features and depth features from the RGB images and depth images, respectively. As the depth features are av... View full abstract»

• ### A Divide-and-Conquer Method for Scalable Robust Multitask Learning

Publication Year: 2015, Page(s):3163 - 3175
Cited by:  Papers (4)
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Multitask learning (MTL) aims at improving the generalization performance of multiple tasks by exploiting the shared factors among them. An important line of research in the MTL is the robust MTL (RMTL) methods, which use trace-norm regularization to capture task relatedness via a low-rank structure. The existing algorithms for the RMTL optimization problems rely on the accelerated proximal gradie... View full abstract»

• ### Learning a Tracking and Estimation Integrated Graphical Model for Human Pose Tracking

Publication Year: 2015, Page(s):3176 - 3186
Cited by:  Papers (13)
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We investigate the tracking of 2-D human poses in a video stream to determine the spatial configuration of body parts in each frame, but this is not a trivial task because people may wear different kinds of clothing and may move very quickly and unpredictably. The technology of pose estimation is typically applied, but it ignores the temporal context and cannot provide smooth, reliable tracking re... View full abstract»

• ### Entropic One-Class Classifiers

Publication Year: 2015, Page(s):3187 - 3200
Cited by:  Papers (13)
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The one-class classification problem is a well-known research endeavor in pattern recognition. The problem is also known under different names, such as outlier and novelty/anomaly detection. The core of the problem consists in modeling and recognizing patterns belonging only to a so-called target class. All other patterns are termed nontarget, and therefore, they should be recognized as such. In t... View full abstract»

• ### Undamped Oscillations Generated by Hopf Bifurcations in Fractional-Order Recurrent Neural Networks With Caputo Derivative

Publication Year: 2015, Page(s):3201 - 3214
Cited by:  Papers (39)
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In this paper, a fractional-order recurrent neural network is proposed and several topics related to the dynamics of such a network are investigated, such as the stability, Hopf bifurcations, and undamped oscillations. The stability domain of the trivial steady state is completely characterized with respect to network parameters and orders of the commensurate-order neural network. Based on the sta... View full abstract»

• ### Synchronization of Neural Networks With Control Packet Loss and Time-Varying Delay via Stochastic Sampled-Data Controller

Publication Year: 2015, Page(s):3215 - 3226
Cited by:  Papers (22)
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This paper addresses the problem of exponential synchronization of neural networks with time-varying delays. A sampled-data controller with stochastically varying sampling intervals is considered. The novelty of this paper lies in the fact that the control packet loss from the controller to the actuator is considered, which may occur in many real-world situations. Sufficient conditions for the exp... View full abstract»

• ### A Complex-Valued Projection Neural Network for Constrained Optimization of Real Functions in Complex Variables

Publication Year: 2015, Page(s):3227 - 3238
Cited by:  Papers (14)
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In this paper, we present a complex-valued projection neural network for solving constrained convex optimization problems of real functions with complex variables, as an extension of real-valued projection neural networks. Theoretically, by developing results on complex-valued optimization techniques, we prove that the complex-valued projection neural network is globally stable and convergent to t... View full abstract»

• ### Pinning Synchronization of Directed Networks With Switching Topologies: A Multiple Lyapunov Functions Approach

Publication Year: 2015, Page(s):3239 - 3250
Cited by:  Papers (110)
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This paper studies the global pinning synchronization problem for a class of complex networks with switching directed topologies. The common assumption in the existing related literature that each possible network topology contains a directed spanning tree is removed in this paper. Using tools from M-matrix theory and stability analysis of the switched nonlinear systems, a new kind of network topo... View full abstract»

• ### Neural-Dynamic-Method-Based Dual-Arm CMG Scheme With Time-Varying Constraints Applied to Humanoid Robots

Publication Year: 2015, Page(s):3251 - 3262
Cited by:  Papers (29)
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We propose a dual-arm cyclic-motion-generation (DACMG) scheme by a neural-dynamic method, which can remedy the joint-angle-drift phenomenon of a humanoid robot. In particular, according to a neural-dynamic design method, first, a cyclic-motion performance index is exploited and applied. This cyclic-motion performance index is then integrated into a quadratic programming (QP)-type scheme with time-... 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