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IEEE Transactions on Neural Networks and Learning Systems

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Displaying Results 1 - 25 of 29

Publication Year: 2017, Page(s):C1 - 245
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• IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Publication Year: 2017, Page(s): C2
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• Hierarchical Change-Detection Tests

Publication Year: 2017, Page(s):246 - 258
Cited by:  Papers (7)
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We present hierarchical change-detection tests (HCDTs), as effective online algorithms for detecting changes in datastreams. HCDTs are characterized by a hierarchical architecture composed of a detection layer and a validation layer. The detection layer steadily analyzes the input datastream by means of an online, sequential CDT, which operates as a low-complexity trigger that promptly detects pos... View full abstract»

• Stability Analysis of Neural Networks With Two Delay Components Based on Dynamic Delay Interval Method

Publication Year: 2017, Page(s):259 - 267
Cited by:  Papers (9)
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In this paper, a dynamic delay interval (DDI) method is proposed to deal with the stability problem of neural networks with two delay components. This method extends the fixed interval of a time-varying delay to a dynamic one, which relaxes the restriction on upper and lower bounds of the delay intervals. Combining the reciprocally convex combination technique and Wirtinger integral inequality, th... View full abstract»

• Asynchronous Dissipative State Estimation for Stochastic Complex Networks With Quantized Jumping Coupling and Uncertain Measurements

Publication Year: 2017, Page(s):268 - 277
Cited by:  Papers (66)
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This paper addresses the problem of state estimation for a class of discrete-time stochastic complex networks with a constrained and randomly varying coupling and uncertain measurements. The randomly varying coupling is governed by a Markov chain, and the capacity constraint is handled by introducing a logarithmic quantizer. The uncertainty of measurements is modeled by a multiplicative noise. An ... View full abstract»

• A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification

Publication Year: 2017, Page(s):278 - 293
Cited by:  Papers (10)
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Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embed... View full abstract»

• Asymmetric Actuator Backlash Compensation in Quantized Adaptive Control of Uncertain Networked Nonlinear Systems

Publication Year: 2017, Page(s):294 - 307
Cited by:  Papers (8)
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This paper mainly aims at the problem of adaptive quantized control for a class of uncertain nonlinear systems preceded by asymmetric actuator backlash. One challenging problem that blocks the construction of our control scheme is that the real control signal is wrapped in the coupling of quantization effect and nonsmooth backlash nonlinearity. To resolve this challenge, this paper presents a two-... View full abstract»

• A Graph-Embedding Approach to Hierarchical Visual Word Mergence

Publication Year: 2017, Page(s):308 - 320
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Appropriately merging visual words are an effective dimension reduction method for the bag-of-visual-words model in image classification. The approach of hierarchically merging visual words has been extensively employed, because it gives a fully determined merging hierarchy. Existing supervised hierarchical merging methods take different approaches and realize the merging process with various form... View full abstract»

• Identification and Control for Singularly Perturbed Systems Using Multitime-Scale Neural Networks

Publication Year: 2017, Page(s):321 - 333
Cited by:  Papers (1)
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Many well-established singular perturbation theories for singularly perturbed systems require the full knowledge of system model parameters. In order to obtain an accurate and faithful model, a new identification scheme for singularly perturbed nonlinear system using multitime-scale recurrent high-order neural networks (NNs) is proposed in this paper. Inspired by the optimal bounded ellipsoid algo... View full abstract»

• Out-of-Sample Extensions for Non-Parametric Kernel Methods

Publication Year: 2017, Page(s):334 - 345
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Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel meth... View full abstract»

• Extended Dissipative State Estimation for Markov Jump Neural Networks With Unreliable Links

Publication Year: 2017, Page(s):346 - 358
Cited by:  Papers (82)
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This paper is concerned with the problem of extended dissipativity-based state estimation for discrete-time Markov jump neural networks (NNs), where the variation of the piecewise time-varying transition probabilities of Markov chain is subject to a set of switching signals satisfying an average dwell-time property. The communication links between the NNs and the estimator are assumed to be imperf... View full abstract»

• A Novel Twin Support-Vector Machine With Pinball Loss

Publication Year: 2017, Page(s):359 - 370
Cited by:  Papers (6)
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Twin support-vector machine (TSVM), which generates two nonparallel hyperplanes by solving a pair of smaller-sized quadratic programming problems (QPPs) instead of a single larger-sized QPP, works faster than the standard SVM, especially for the large-scale data sets. However, the traditional TSVM adopts hinge loss which easily leads to its sensitivity of the noise and instability for resampling. ... View full abstract»

• Closed-Loop Modulation of the Pathological Disorders of the Basal Ganglia Network

Publication Year: 2017, Page(s):371 - 382
Cited by:  Papers (2)
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A generalized predictive closed-loop control strategy to improve the basal ganglia activity patterns in Parkinson's disease (PD) is explored in this paper. Based on system identification, an input-output model is established to reveal the relationship between external stimulation and neuronal responses. The model contributes to the implementation of the generalized predictive control (GPC) algorit... View full abstract»

• QRNN: $q$ -Generalized Random Neural Network

Publication Year: 2017, Page(s):383 - 390
Cited by:  Papers (1)
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Artificial neural networks (ANNs) are widely used in applications with complex decision boundaries. A large number of activation functions have been proposed in the literature to achieve better representations of the observed data. However, only a few works employ Tsallis statistics, which has successfully been applied to various other fields. This paper presents a random neural network (RNN) with... View full abstract»

• Growing Echo-State Network With Multiple Subreservoirs

Publication Year: 2017, Page(s):391 - 404
Cited by:  Papers (2)
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An echo-state network (ESN) is an effective alternative to gradient methods for training recurrent neural network. However, it is difficult to determine the structure (mainly the reservoir) of the ESN to match with the given application. In this paper, a growing ESN (GESN) is proposed to design the size and topology of the reservoir automatically. First, the GESN makes use of the block matrix theo... View full abstract»

• A Scoring Scheme for Online Feature Selection: Simulating Model Performance Without Retraining

Publication Year: 2017, Page(s):405 - 414
Cited by:  Papers (1)
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Increasing the number of features increases the complexity of a model even if the additional feature does not improve its decision-making capacity. Irrelevant features may also cause overfitting and reduce interpretability of the concerned model. It is, therefore, important that the features are optimally selected before a model is built. In the case of online learning, new instances are periodica... View full abstract»

• Distributed Recurrent Neural Networks for Cooperative Control of Manipulators: A Game-Theoretic Perspective

Publication Year: 2017, Page(s):415 - 426
Cited by:  Papers (15)
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This paper considers cooperative kinematic control of multiple manipulators using distributed recurrent neural networks and provides a tractable way to extend existing results on individual manipulator control using recurrent neural networks to the scenario with the coordination of multiple manipulators. The problem is formulated as a constrained game, where energy consumptions for each manipulato... View full abstract»

• Graph Theory-Based Pinning Synchronization of Stochastic Complex Dynamical Networks

Publication Year: 2017, Page(s):427 - 437
Cited by:  Papers (3)
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This paper is concerned with the adaptive pinning synchronization problem of stochastic complex dynamical networks (CDNs). Based on algebraic graph theory and Lyapunov theory, pinning controller design conditions are derived, and the rigorous convergence analysis of synchronization errors in the probability sense is also conducted. Compared with the existing results, the topology structures of sto... View full abstract»

• Learning a Coupled Linearized Method in Online Setting

Publication Year: 2017, Page(s):438 - 450
Cited by:  Papers (1)
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Based on the alternating direction method of multipliers, in this paper, we propose, analyze, and test a coupled linearized method, which aims to minimize an unconstrained problem consisting of a loss term and a regularization term in an online setting. To solve this problem, we first transform it into an equivalent constrained minimization problem with a separable structure. Then, we split the co... View full abstract»

• Cross-Modality Feature Learning Through Generic Hierarchical Hyperlingual-Words

Publication Year: 2017, Page(s):451 - 463
Cited by:  Papers (1)
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Recognizing facial images captured under visible light has long been discussed in the past decades. However, there are many impact factors that hinder its successful application in real-world, e.g., illumination, pose variations. Recent work has concentrated on different spectrals, i.e., near infrared, that can only be perceived by specifically designed device to avoid the illumination problem. Ho... View full abstract»

• Identification of Boolean Networks Using Premined Network Topology Information

Publication Year: 2017, Page(s):464 - 469
Cited by:  Papers (1)
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This brief aims to reduce the data requirement for the identification of Boolean networks (BNs) by using the premined network topology information. First, a matching table is created and used for sifting the true from the false dependences among the nodes in the BNs. Then, a dynamic extension to matching table is developed to enable the dynamic locating of matching pairs to start as soon as possib... View full abstract»

• A Proposal for Local $k$ Values for $k$ -Nearest Neighbor Rule

Publication Year: 2017, Page(s):470 - 475
Cited by:  Papers (4)
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The k-nearest neighbor (k-NN) classifier is one of the most widely used methods of classification due to several interesting features, including good generalization and easy implementation. Although simple, it is usually able to match and even outperform more sophisticated and complex methods. One of the problems with this approach is fixing the appropriate value of k. Although a good value might ... View full abstract»

• Impulsive Effects and Stability Analysis on Memristive Neural Networks With Variable Delays

Publication Year: 2017, Page(s):476 - 481
Cited by:  Papers (3)
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In this brief, hybrid impulsive and adaptive feedback controllers are simultaneously exerted on a general delayed memristive neural network (MNN) model to formulate a novel impulsive controlled MNN (IMNN) model with variable delays. By means of Lyapunov-Razumikhin technique and other analytical ways, several new stability criteria of the proposed IMNN model are obtained. In addition, by choosing a... View full abstract»

• Neural Network-Based DOBC for a Class of Nonlinear Systems With Unmatched Disturbances

Publication Year: 2017, Page(s):482 - 489
Cited by:  Papers (11)
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In this brief, the problem of composite anti-disturbance tracking control for a class of strict-feedback systems with unmatched unknown nonlinear functions and external disturbances is investigated. A disturbance-observer-based control (DOBC) in combination with a neural network scheme and back-stepping method is developed to achieve a composite anti-disturbance controller design that provides gua... View full abstract»

• IEEE Transactions on Neural Networks and Learning Systems special section on deep reinforcement learning and adaptive dynamic programming

Publication Year: 2017, Page(s): 490
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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