# IEEE Transactions on Neural Networks and Learning Systems

## Volume 29 Issue 4 • April 2018

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## Filter Results

Displaying Results 1 - 25 of 57

Publication Year: 2018, Page(s):C1 - 765
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• ### IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Publication Year: 2018, Page(s): C2
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• ### Multicolumn RBF Network

Publication Year: 2018, Page(s):766 - 778
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This paper proposes the multicolumn RBF network (MCRN) as a method to improve the accuracy and speed of a traditional radial basis function network (RBFN). The RBFN, as a fully connected artificial neural network (ANN), suffers from costly kernel inner-product calculations due to the use of many instances as the centers of hidden units. This issue is not critical for small datasets, as adding more... View full abstract»

• ### A Regularized SNPOM for Stable Parameter Estimation of RBF-AR(X) Model

Publication Year: 2018, Page(s):779 - 791
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Recently, the radial basis function (RBF) network-style coefficients AutoRegressive (with exogenous inputs) [RBF-AR(X)] model identified by the structured nonlinear parameter optimization method (SNPOM) has attracted considerable interest because of its significant performance in nonlinear system modeling. However, this promising technique may occasionally confront the problem that the parameters ... View full abstract»

• ### A Fast Algorithm of Convex Hull Vertices Selection for Online Classification

Publication Year: 2018, Page(s):792 - 806
Cited by:  Papers (1)
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Reducing samples through convex hull vertices selection (CHVS) within each class is an important and effective method for online classification problems, since the classifier can be trained rapidly with the selected samples. However, the process of CHVS is NP-hard. In this paper, we propose a fast algorithm to select the convex hull vertices, based on the convex hull decomposition and the property... View full abstract»

• ### Adaptive Antisynchronization of Multilayer Reaction–Diffusion Neural Networks

Publication Year: 2018, Page(s):807 - 818
Cited by:  Papers (2)
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In this paper, an antisynchronization problem is considered for an array of linearly coupled reaction-diffusion neural networks with cooperative-competitive interactions and time-varying coupling delays. The interaction topology among the neural nodes is modeled by a multilayer signed graph. The state evolution of a neuron in each layer of the coupled neural network is described by a reaction-diff... View full abstract»

• ### Synchronization for the Realization-Dependent Probabilistic Boolean Networks

Publication Year: 2018, Page(s):819 - 831
Cited by:  Papers (2)
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This paper investigates the synchronization problem for the realization-dependent probabilistic Boolean networks (PBNs) coupled unidirectionally in the drive-response configuration. The realization of the response PBN is assumed to be uniquely determined by the realization signal generated by the drive PBN at each discrete time instant. First, the drive-response PBNs are expressed in their algebra... View full abstract»

• ### Adaptive Tracking Control for Robots With an Interneural Computing Scheme

Publication Year: 2018, Page(s):832 - 844
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Adaptive tracking control of mobile robots requires the ability to follow a trajectory generated by a moving target. The conventional analysis of adaptive tracking uses energy minimization to study the convergence and robustness of the tracking error when the mobile robot follows a desired trajectory. However, in the case that the moving target generates trajectories with uncertainties, a common L... View full abstract»

• ### Robust Estimation for Neural Networks With Randomly Occurring Distributed Delays and Markovian Jump Coupling

Publication Year: 2018, Page(s):845 - 855
Cited by:  Papers (18)
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This paper studies the issue of robust state estimation for coupled neural networks with parameter uncertainty and randomly occurring distributed delays, where the polytopic model is employed to describe the parameter uncertainty. A set of Bernoulli processes with different stochastic properties are introduced to model the randomly occurrences of the distributed delays. Novel state estimators base... View full abstract»

• ### Finite-Time Stabilization of Delayed Memristive Neural Networks: Discontinuous State-Feedback and Adaptive Control Approach

Publication Year: 2018, Page(s):856 - 868
Cited by:  Papers (2)
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In this paper, a general class of delayed memristive neural networks (DMNNs) system described by functional differential equation with discontinuous right-hand side is considered. Under the extended Filippov-framework, we investigate the finite-time stabilization problem for DMNNs by using the famous finite-time stability theorem and the generalized Lyapunov functional method. To do so, we design ... View full abstract»

• ### Identifying a Probabilistic Boolean Threshold Network From Samples

Publication Year: 2018, Page(s):869 - 881
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This paper studies the problem of exactly identifying the structure of a probabilistic Boolean network (PBN) from a given set of samples, where PBNs are probabilistic extensions of Boolean networks. Cheng et al. studied the problem while focusing on PBNs consisting of pairs of AND/OR functions. This paper considers PBNs consisting of Boolean threshold functions while focusing on those threshold fu... View full abstract»

• ### Online Nonlinear AUC Maximization for Imbalanced Data Sets

Publication Year: 2018, Page(s):882 - 895
Cited by:  Papers (2)
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Classifying binary imbalanced streaming data is a significant task in both machine learning and data mining. Previously, online area under the receiver operating characteristic (ROC) curve (AUC) maximization has been proposed to seek a linear classifier. However, it is not well suited for handling nonlinearity and heterogeneity of the data. In this paper, we propose the kernelized online imbalance... View full abstract»

• ### Feature Combination via Clustering

Publication Year: 2018, Page(s):896 - 907
Cited by:  Papers (2)
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In image classification, feature combination is often used to combine the merits of multiple complementary features and improve the classification accuracy compared with one single feature. Existing feature combination algorithms, e.g., multiple kernel learning, usually determine the weights of features based on the optimization with respect to some classifier-dependent objective function. These a... View full abstract»

• ### Impulsive Effects on Quasi-Synchronization of Neural Networks With Parameter Mismatches and Time-Varying Delay

Publication Year: 2018, Page(s):908 - 919
Cited by:  Papers (3)
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This paper is concerned with the exponential synchronization issue of nonidentically coupled neural networks with time-varying delay. Due to the parameter mismatch phenomena existed in neural networks, the problem of quasi-synchronization is thus discussed by applying some impulsive control strategies. Based on the definition of average impulsive interval and the extended comparison principle for ... View full abstract»

• ### Multivariate Time-Series Classification Using the Hidden-Unit Logistic Model

Publication Year: 2018, Page(s):920 - 931
Cited by:  Papers (1)
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We present a new model for multivariate time-series classification, called the hidden-unit logistic model (HULM), that uses binary stochastic hidden units to model latent structure in the data. The hidden units are connected in a chain structure that models temporal dependencies in the data. Compared with the prior models for time-series classification such as the hidden conditional random field, ... View full abstract»

• ### Manifold Regularized Reinforcement Learning

Publication Year: 2018, Page(s):932 - 943
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This paper introduces a novel manifold regularized reinforcement learning scheme for continuous Markov decision processes. Smooth feature representations for value function approximation can be automatically learned using the unsupervised manifold regularization method. The learned features are data-driven, and can be adapted to the geometry of the state space. Furthermore, the scheme provides a d... View full abstract»

• ### Adaptive Unsupervised Feature Selection With Structure Regularization

Publication Year: 2018, Page(s):944 - 956
Cited by:  Papers (5)
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Feature selection is one of the most important dimension reduction techniques for its efficiency and interpretation. Since practical data in large scale are usually collected without labels, and labeling these data are dramatically expensive and time-consuming, unsupervised feature selection has become a ubiquitous and challenging problem. Without label information, the fundamental problem of unsu... View full abstract»

• ### Adaptive Dynamic Programming for Discrete-Time Zero-Sum Games

Publication Year: 2018, Page(s):957 - 969
Cited by:  Papers (4)
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In this paper, a novel adaptive dynamic programming (ADP) algorithm, called “iterative zero-sum ADP algorithm,” is developed to solve infinite-horizon discrete-time two-player zero-sum games of nonlinear systems. The present iterative zero-sum ADP algorithm permits arbitrary positive semidefinite functions to initialize the upper and lower iterations. A novel convergence analysis is developed to g... View full abstract»

• ### Quantization-Based Adaptive Actor-Critic Tracking Control With Tracking Error Constraints

Publication Year: 2018, Page(s):970 - 980
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In this paper, the problem of adaptive actor-critic (AC) tracking control is investigated for a class of continuous-time nonlinear systems with unknown nonlinearities and quantized inputs. Different from the existing results based on reinforcement learning, the tracking error constraints are considered and new critic functions are constructed to improve the performance further. To ensure that the ... View full abstract»

• ### A Collaborative Neurodynamic Approach to Multiple-Objective Distributed Optimization

Publication Year: 2018, Page(s):981 - 992
Cited by:  Papers (8)
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This paper is concerned with multiple-objective distributed optimization. Based on objective weighting and decision space decomposition, a collaborative neurodynamic approach to multiobjective distributed optimization is presented. In the approach, a system of collaborative neural networks is developed to search for Pareto optimal solutions, where each neural network is associated with one objecti... View full abstract»

• ### On Mixed Data and Event Driven Design for Adaptive-Critic-Based Nonlinear $H_{\infty}$ Control

Publication Year: 2018, Page(s):993 - 1005
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In this paper, based on the adaptive critic learning technique, the H∞control for a class of unknown nonlinear dynamic systems is investigated by adopting a mixed data and event driven design approach. The nonlinear H∞control problem is formulated as a two-player zero-sum differential game and the adaptive critic method is employed to cope with the data-based optimization. Th... View full abstract»

• ### Regularized Label Relaxation Linear Regression

Publication Year: 2018, Page(s):1006 - 1018
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Linear regression (LR) and some of its variants have been widely used for classification problems. Most of these methods assume that during the learning phase, the training samples can be exactly transformed into a strict binary label matrix, which has too little freedom to fit the labels adequately. To address this problem, in this paper, we propose a novel regularized label relaxation LR method,... View full abstract»

• ### Collaborative Random Faces-Guided Encoders for Pose-Invariant Face Representation Learning

Publication Year: 2018, Page(s):1019 - 1032
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Learning discriminant face representation for pose-invariant face recognition has been identified as a critical issue in visual learning systems. The challenge lies in the drastic changes of facial appearances between the test face and the registered face. To that end, we propose a high-level feature learning framework called “collaborative random faces (RFs)-guided encoders” toward this problem. ... View full abstract»

• ### Model-Based Adaptive Event-Triggered Control of Strict-Feedback Nonlinear Systems

Publication Year: 2018, Page(s):1033 - 1045
Cited by:  Papers (10)
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This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN... View full abstract»

• ### Finite-Time State Estimation for Recurrent Delayed Neural Networks With Component-Based Event-Triggering Protocol

Publication Year: 2018, Page(s):1046 - 1057
Cited by:  Papers (3)
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This paper deals with the event-based finite-time state estimation problem for a class of discrete-time stochastic neural networks with mixed discrete and distributed time delays. In order to mitigate the burden of data communication, a general component-based event-triggered transmission mechanism is proposed to determine whether the measurement output should be released to the estimator at certa... 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