# IEEE Transactions on Neural Networks and Learning Systems

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Displaying Results 1 - 23 of 23

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

Publication Year: 2015, Page(s): C2
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• ### Fractional Extreme Value Adaptive Training Method: Fractional Steepest Descent Approach

Publication Year: 2015, Page(s):653 - 662
Cited by:  Papers (22)
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The application of fractional calculus to signal processing and adaptive learning is an emerging area of research. A novel fractional adaptive learning approach that utilizes fractional calculus is presented in this paper. In particular, a fractional steepest descent approach is proposed. A fractional quadratic energy norm is studied, and the stability and convergence of our proposed method are an... View full abstract»

• ### Quaternion-Valued Echo State Networks

Publication Year: 2015, Page(s):663 - 673
Cited by:  Papers (15)
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Quaternion-valued echo state networks (QESNs) are introduced to cater for 3-D and 4-D processes, such as those observed in the context of renewable energy (3-D wind modeling) and human centered computing (3-D inertial body sensors). The introduction of QESNs is made possible by the recent emergence of quaternion nonlinear activation functions with local analytic properties, required by nonlinear g... View full abstract»

• ### Successive Overrelaxation for Laplacian Support Vector Machine

Publication Year: 2015, Page(s):674 - 683
Cited by:  Papers (7)
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Semisupervised learning (SSL) problem, which makes use of both a large amount of cheap unlabeled data and a few unlabeled data for training, in the last few years, has attracted amounts of attention in machine learning and data mining. Exploiting the manifold regularization (MR), Belkinet al. proposed a new semisupervised classification algorithm: Laplacian support vector machines (LapSVMs), and h... View full abstract»

• ### Adaptive Optimal Control of Highly Dissipative Nonlinear Spatially Distributed Processes With Neuro-Dynamic Programming

Publication Year: 2015, Page(s):684 - 696
Cited by:  Papers (34)
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Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to describe the system dynamics of industrial spatially distributed processes (SDPs). In this paper, we consider the optimal control problem of the general highly dissipative SDPs, and propose an adaptive optimal control approach based on neuro-dynamic programming (NDP). Initially, Karhunen-Loève decompo... View full abstract»

• ### Convolutive Bounded Component Analysis Algorithms for Independent and Dependent Source Separation

Publication Year: 2015, Page(s):697 - 708
Cited by:  Papers (8)
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Bounded component analysis (BCA) is a framework that can be considered as a more general framework than independent component analysis (ICA) under the boundedness constraint on sources. Using this framework, it is possible to separate dependent as well as independent components from their mixtures. In this paper, as an extension of a recently introduced instantaneous BCA approach, we introduce a f... View full abstract»

• ### Gaussian Kernel Width Optimization for Sparse Bayesian Learning

Publication Year: 2015, Page(s):709 - 719
Cited by:  Papers (6)
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Sparse kernel methods have been widely used in regression and classification applications. The performance and the sparsity of these methods are dependent on the appropriate choice of the corresponding kernel functions and their parameters. Typically, the kernel parameters are selected using a cross-validation approach. In this paper, a learning method that is an extension of the relevance vector ... View full abstract»

• ### Multiclass Semisupervised Learning Based Upon Kernel Spectral Clustering

Publication Year: 2015, Page(s):720 - 733
Cited by:  Papers (10)
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This paper proposes a multiclass semisupervised learning algorithm by using kernel spectral clustering (KSC) as a core model. A regularized KSC is formulated to estimate the class memberships of data points in a semisupervised setting using the one-versus-all strategy while both labeled and unlabeled data points are present in the learning process. The propagation of the labels to a large amount o... View full abstract»

• ### Impulsive Stabilization and Impulsive Synchronization of Discrete-Time Delayed Neural Networks

Publication Year: 2015, Page(s):734 - 748
Cited by:  Papers (39)
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This paper investigates the problems of impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks (DDNNs). Two types of DDNNs with stabilizing impulses are studied. By introducing the time-varying Lyapunov functional to capture the dynamical characteristics of discrete-time impulsive delayed neural networks (DIDNNs) and by using a convex combination technique, ... View full abstract»

• ### A Universal Concept Based on Cellular Neural Networks for Ultrafast and Flexible Solving of Differential Equations

Publication Year: 2015, Page(s):749 - 762
Cited by:  Papers (6)
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This paper develops and validates a comprehensive and universally applicable computational concept for solving nonlinear differential equations (NDEs) through a neurocomputing concept based on cellular neural networks (CNNs). High-precision, stability, convergence, and lowest-possible memory requirements are ensured by the CNN processor architecture. A significant challenge solved in this paper is... View full abstract»

• ### On Learning Navigation Behaviors for Small Mobile Robots With Reservoir Computing Architectures

Publication Year: 2015, Page(s):763 - 780
Cited by:  Papers (9)
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This paper proposes a general reservoir computing (RC) learning framework that can be used to learn navigation behaviors for mobile robots in simple and complex unknown partially observable environments. RC provides an efficient way to train recurrent neural networks by letting the recurrent part of the network (called reservoir) be fixed while only a linear readout output layer is trained. The pr... View full abstract»

• ### Coupled Attribute Similarity Learning on Categorical Data

Publication Year: 2015, Page(s):781 - 797
Cited by:  Papers (8)
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Attribute independence has been taken as a major assumption in the limited research that has been conducted on similarity analysis for categorical data, especially unsupervised learning. However, in real-world data sources, attributes are more or less associated with each other in terms of certain coupling relationships. Accordingly, recent works on attribute dependency aggregation have introduced... View full abstract»

• ### Topology-Based Clustering Using Polar Self-Organizing Map

Publication Year: 2015, Page(s):798 - 808
Cited by:  Papers (1)
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Cluster analysis of unlabeled data sets has been recognized as a key research topic in varieties of fields. In many practical cases, no a priori knowledge is specified, for example, the number of clusters is unknown. In this paper, grid clustering based on the polar self-organizing map (PolSOM) is developed to automatically identify the optimal number of partitions. The data topology consisting of... View full abstract»

• ### Robust Consensus Tracking Control for Multiagent Systems With Initial State Shifts, Disturbances, and Switching Topologies

Publication Year: 2015, Page(s):809 - 824
Cited by:  Papers (18)
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This paper deals with the consensus tracking control issues of multiagent systems and aims to solve them as accurately as possible over a finite time interval through an iterative learning approach. Based on the iterative rule, distributed algorithms are proposed for every agent using its nearest neighbor knowledge, for which the robustness problem is addressed against initial state shifts, distur... View full abstract»

• ### $L_{1}$ -Norm Low-Rank Matrix Factorization by Variational Bayesian Method

Publication Year: 2015, Page(s):825 - 839
Cited by:  Papers (14)
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The L1-norm low-rank matrix factorization (LRMF) has been attracting much attention due to its wide applications to computer vision and pattern recognition. In this paper, we construct a new hierarchical Bayesian generative model for the L1-norm LRMF problem and design a mean-field variational method to automatically infer all the parameters involved in the model by closed-fo... View full abstract»

• ### Nonlinear Model Predictive Control Based on Collective Neurodynamic Optimization

Publication Year: 2015, Page(s):840 - 850
Cited by:  Papers (4)
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In general, nonlinear model predictive control (NMPC) entails solving a sequential global optimization problem with a nonconvex cost function or constraints. This paper presents a novel collective neurodynamic optimization approach to NMPC without linearization. Utilizing a group of recurrent neural networks (RNNs), the proposed collective neurodynamic optimization approach searches for optimal so... View full abstract»

• ### Multiple Actor-Critic Structures for Continuous-Time Optimal Control Using Input-Output Data

Publication Year: 2015, Page(s):851 - 865
Cited by:  Papers (52)
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In industrial process control, there may be multiple performance objectives, depending on salient features of the input-output data. Aiming at this situation, this paper proposes multiple actor-critic structures to obtain the optimal control via input-output data for unknown nonlinear systems. The shunting inhibitory artificial neural network (SIANN) is used to classify the input-output data into ... View full abstract»

• ### Infinite Horizon Self-Learning Optimal Control of Nonaffine Discrete-Time Nonlinear Systems

Publication Year: 2015, Page(s):866 - 879
Cited by:  Papers (56)
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In this paper, a novel iterative adaptive dynamic programming (ADP)-based infinite horizon self-learning optimal control algorithm, called generalized policy iteration algorithm, is developed for nonaffine discrete-time (DT) nonlinear systems. Generalized policy iteration algorithm is a general idea of interacting policy and value iteration algorithms of ADP. The developed generalized policy itera... View full abstract»

• ### A Bootstrap Based Neyman-Pearson Test for Identifying Variable Importance

Publication Year: 2015, Page(s):880 - 886
Cited by:  Papers (4)
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Selection of most informative features that leads to a small loss on future data are arguably one of the most important steps in classification, data analysis and model selection. Several feature selection (FS) algorithms are available; however, due to noise present in any data set, FS algorithms are typically accompanied by an appropriate cross-validation scheme. In this brief, we propose a stati... View full abstract»

• ### IEEE Xplore Digital Library

Publication Year: 2015, Page(s): 888
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• ### IEEE Computational Intelligence Society Information

Publication Year: 2015, Page(s): C3
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• ### IEEE Transactions on Neural Networks information for authors

Publication Year: 2015, Page(s): C4
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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