Volume 26 Issue 3 • March 2015
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Table of contents
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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An Enhanced Fuzzy Min–Max Neural Network for Pattern Classification
Publication Year: 2015, Page(s):417 - 429
Cited by: Papers (14)An enhanced fuzzy min-max (EFMM) network is proposed for pattern classification in this paper. The aim is to overcome a number of limitations of the original fuzzy min-max (FMM) network and improve its classification performance. The key contributions are three heuristic rules to enhance the learning algorithm of FMM. First, a new hyperbox expansion rule to eliminate the overlapping problem during... View full abstract»
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ML-TREE: A Tree-Structure-Based Approach to Multilabel Learning
Publication Year: 2015, Page(s):430 - 443
Cited by: Papers (6)Multilabel learning aims to predict labels of unseen instances by learning from training samples that are associated with a set of known labels. In this paper, we propose to use a hierarchical tree model for multilabel learning, and to develop the ML-Tree algorithm for finding the tree structure. ML-Tree considers a tree as a hierarchy of data and constructs the tree using the induction of one-aga... View full abstract»
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Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines
Publication Year: 2015, Page(s):444 - 457
Cited by: Papers (12)When the amount of labeled data are limited, semisupervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the underlying data manifold. By approximating this manifold as a weighted graph, such graph-based techniques can often achieve state-of-the-art performance... View full abstract»
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Dimension Selective Self-Organizing Maps With Time-Varying Structure for Subspace and Projected Clustering
Publication Year: 2015, Page(s):458 - 471
Cited by: Papers (1)Subspace clustering is the task of identifying clusters in subspaces of the input dimensions of a given dataset. Noisy data in certain attributes cause difficulties for traditional clustering algorithms, because the high discrepancies within them can make objects appear too different to be grouped in the same cluster. This requires methods specially designed for subspace clustering. This paper pre... View full abstract»
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Neural Network-Based Finite Horizon Stochastic Optimal Control Design for Nonlinear Networked Control Systems
Publication Year: 2015, Page(s):472 - 485
Cited by: Papers (8)The stochastic optimal control of nonlinear networked control systems (NNCSs) using neuro-dynamic programming (NDP) over a finite time horizon is a challenging problem due to terminal constraints, system uncertainties, and unknown network imperfections, such as network-induced delays and packet losses. Since the traditional iteration or time-based infinite horizon NDP schemes are unsuitable for NN... View full abstract»
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Neural Network-Based Finite-Horizon Optimal Control of Uncertain Affine Nonlinear Discrete-Time Systems
Publication Year: 2015, Page(s):486 - 499
Cited by: Papers (11)In this paper, the finite-horizon optimal control design for nonlinear discrete-time systems in affine form is presented. In contrast with the traditional approximate dynamic programming methodology, which requires at least partial knowledge of the system dynamics, in this paper, the complete system dynamics are relaxed utilizing a neural network (NN)-based identifier to learn the control coeffici... View full abstract»
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Maintaining the Integrity of Sources in Complex Learning Systems: Intraference and the Correlation Preserving Transform
Publication Year: 2015, Page(s):500 - 509
Cited by: Papers (1)The correlation preserving transform (CPT) is introduced to perform bivariate component analysis via decorrelating matrix decompositions, while at the same time preserving the integrity of original bivariate sources. Specifically, unlike existing bivariate uncorrelating matrix decomposition techniques, CPT is designed to preserve both the order of the data channels within every bivariate source an... View full abstract»
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Exponential Synchronization of Complex Networks of Linear Systems and Nonlinear Oscillators: A Unified Analysis
Publication Year: 2015, Page(s):510 - 521
Cited by: Papers (56)A unified approach to the analysis of synchronization for complex dynamical networks, i.e., networks of partial-state coupled linear systems and networks of full-state coupled nonlinear oscillators, is introduced. It is shown that the developed analysis can be used to describe the difference between the state of each node and the weighted sum of the states of those nodes playing the role of leader... View full abstract»
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Spatio-Temporal Learning With the Online Finite and Infinite Echo-State Gaussian Processes
Publication Year: 2015, Page(s):522 - 536
Cited by: Papers (7)Successful biological systems adapt to change. In this paper, we are principally concerned with adaptive systems that operate in environments where data arrives sequentially and is multivariate in nature, for example, sensory streams in robotic systems. We contribute two reservoir inspired methods: 1) the online echostate Gaussian process (OESGP) and 2) its infinite variant, the online infinite ec... View full abstract»
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Bayesian Nonparametric Adaptive Control Using Gaussian Processes
Publication Year: 2015, Page(s):537 - 550
Cited by: Papers (10)Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the ex... View full abstract»
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Generalization Performance of Radial Basis Function Networks
Publication Year: 2015, Page(s):551 - 564
Cited by: Papers (6)This paper studies the generalization performance of radial basis function (RBF) networks using local Rademacher complexities. We propose a general result on controlling local Rademacher complexities with the L1 -metric capacity. We then apply this result to estimate the RBF networks' complexities, based on which a novel estimation error bound is obtained. An effective approximation err... View full abstract»
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Second-Order Global Consensus in Multiagent Networks With Random Directional Link Failure
Publication Year: 2015, Page(s):565 - 575
Cited by: Papers (46)In this paper, we consider the second-order globally nonlinear consensus in a multiagent network with general directed topology and random interconnection failure by characterizing the behavior of stochastic dynamical system with the corresponding time-averaged system. A criterion for the second-order consensus is derived by constructing a Lyapunov function for the time-averaged network. By associ... View full abstract»
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Novelty Detection Using Level Set Methods
Publication Year: 2015, Page(s):576 - 588
Cited by: Papers (6)This paper presents a level set boundary description (LSBD) approach for novelty detection that treats the nonlinear boundary directly in the input space. The proposed approach consists of level set function (LSF) construction, boundary evolution, and termination of the training process. It employs kernel density estimation to construct the LSF of the initial boundary for the training data set. Th... View full abstract»
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A Simplified Adaptive Neural Network Prescribed Performance Controller for Uncertain MIMO Feedback Linearizable Systems
Publication Year: 2015, Page(s):589 - 600
Cited by: Papers (24)In this paper, the problem of deriving a continuous, state-feedback controller for a class of multiinput multioutput feedback linearizable systems is considered with special emphasis on controller simplification and reduction of the overall design complexity with respect to the current state of the art. The proposed scheme achieves prescribed bounds on the transient and steady-state performance of... View full abstract»
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Convergence Analysis of the FOCUSS Algorithm
Publication Year: 2015, Page(s):601 - 613
Cited by: Papers (4)Focal Underdetermined System Solver (FOCUSS) is a powerful and easy to implement tool for basis selection and inverse problems. One of the fundamental problems regarding this method is its convergence, which remains unsolved until now. We investigate the convergence of the FOCUSS algorithm in this paper. We first give a rigorous derivation for the FOCUSS algorithm by exploiting the auxiliary funct... View full abstract»
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GrDHP: A General Utility Function Representation for Dual Heuristic Dynamic Programming
Publication Year: 2015, Page(s):614 - 627
Cited by: Papers (32)A general utility function representation is proposed to provide the required derivable and adjustable utility function for the dual heuristic dynamic programming (DHP) design. Goal representation DHP (GrDHP) is presented with a goal network being on top of the traditional DHP design. This goal network provides a general mapping between the system states and the derivatives of the utility function... View full abstract»
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The Generalization Ability of Online SVM Classification Based on Markov Sampling
Publication Year: 2015, Page(s):628 - 639
Cited by: Papers (7)In this paper, we consider online support vector machine (SVM) classification learning algorithms with uniformly ergodic Markov chain (u.e.M.c.) samples. We establish the bound on the misclassification error of an online SVM classification algorithm with u.e.M.c. samples based on reproducing kernel Hilbert spaces and obtain a satisfactory convergence rate. We also introduce a novel online SVM clas... View full abstract»
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Neural Network-Based Adaptive Dynamic Surface Control for Permanent Magnet Synchronous Motors
Publication Year: 2015, Page(s):640 - 645
Cited by: Papers (46)This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping de... View full abstract»
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A Unified Approach to Universal Prediction: Generalized Upper and Lower Bounds
Publication Year: 2015, Page(s):646 - 651
Cited by: Papers (3)We study sequential prediction of real-valued, arbitrary, and unknown sequences under the squared error loss as well as the best parametric predictor out of a large, continuous class of predictors. Inspired by recent results from computational learning theory, we refrain from any statistical assumptions and define the performance with respect to the class of general parametric predictors. In parti... View full abstract»
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Special issue on New Developments in Neural Network Structures
Publication Year: 2015, Page(s): 652|
PDF (303 KB)
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IEEE Computational Intelligence Society Information
Publication Year: 2015, Page(s): C3|
PDF (118 KB)
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IEEE Transactions on Neural Networks information for authors
Publication Year: 2015, Page(s): C4|
PDF (128 KB)
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.
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