# IEEE Transactions on Neural Networks and Learning Systems

## Volume 28 Issue 12 • Dec. 2017

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## Filter Results

Displaying Results 1 - 25 of 27

Publication Year: 2017, Page(s):C1 - 2845
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• ### IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Publication Year: 2017, Page(s): C2
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• ### One-Class Classifiers Based on Entropic Spanning Graphs

Publication Year: 2017, Page(s):2846 - 2858
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One-class classifiers offer valuable tools to assess the presence of outliers in data. In this paper, we propose a design methodology for one-class classifiers based on entropic spanning graphs. Our approach also takes into account the possibility to process nonnumeric data by means of an embedding procedure. The spanning graph is learned on the embedded input data, and the outcoming partition of ... View full abstract»

• ### Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices

Publication Year: 2017, Page(s):2859 - 2871
Cited by:  Papers (3)
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Data encoded as symmetric positive definite (SPD) matrices frequently arise in many areas of computer vision and machine learning. While these matrices form an open subset of the Euclidean space of symmetric matrices, viewing them through the lens of non-Euclidean Riemannian (Riem) geometry often turns out to be better suited in capturing several desirable data properties. Inspired by the great su... View full abstract»

• ### Comparative Performance of Complex-Valued B-Spline and Polynomial Models Applied to Iterative Frequency-Domain Decision Feedback Equalization of Hammerstein Channels

Publication Year: 2017, Page(s):2872 - 2884
Cited by:  Papers (1)
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Complex-valued (CV) B-spline neural network approach offers a highly effective means for identifying and inverting practical Hammerstein systems. Compared with its conventional CV polynomial-based counterpart, a CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. This paper reviews the optimality of the CV B-... View full abstract»

• ### Adaptive Exponential Synchronization of Multislave Time-Delayed Recurrent Neural Networks With Lévy Noise and Regime Switching

Publication Year: 2017, Page(s):2885 - 2898
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This paper discusses the problem of adaptive exponential synchronization in mean square for a new neural network model with the following features: 1) the noise is characterized by the Lévy process and the parameters of the model change in line with the Markovian process; 2) the master system is also disturbed by the same Lévy noise; and 3) there are multiple slave systems, and the state matrix of... View full abstract»

• ### Binary Set Embedding for Cross-Modal Retrieval

Publication Year: 2017, Page(s):2899 - 2910
Cited by:  Papers (1)
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Cross-modal retrieval is such a challenging topic that traditional global representations would fail to bridge the semantic gap between images and texts to a satisfactory level. Using local features from images and words from documents directly can be more robust for the scenario with large intraclass variations and small interclass discrepancies. In this paper, we propose a novel unsupervised bin... View full abstract»

• ### Airline Passenger Profiling Based on Fuzzy Deep Machine Learning

Publication Year: 2017, Page(s):2911 - 2923
Cited by:  Papers (2)
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Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such ... View full abstract»

• ### Finite-Time Stability Analysis for Markovian Jump Memristive Neural Networks With Partly Unknown Transition Probabilities

Publication Year: 2017, Page(s):2924 - 2935
Cited by:  Papers (3)
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This paper is concerned with the finite-time stochastically stability (FTSS) analysis of Markovian jump memristive neural networks with partly unknown transition probabilities. In the neural networks, there exist a group of modes determined by Markov chain, and thus, the Markovian jump was taken into consideration and the concept of FTSS is first introduced for the memristive model. By introducing... View full abstract»

• ### Cluster Validation Method for Determining the Number of Clusters in Categorical Sequences

Publication Year: 2017, Page(s):2936 - 2948
Cited by:  Papers (1)
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Cluster validation, which is the process of evaluating the quality of clustering results, plays an important role for practical machine learning systems. Categorical sequences, such as biological sequences in computational biology, have become common in real-world applications. Different from previous studies, which mainly focused on attribute-value data, in this paper, we work on the cluster vali... View full abstract»

• ### Joint Sparse Representation and Embedding Propagation Learning: A Framework for Graph-Based Semisupervised Learning

Publication Year: 2017, Page(s):2949 - 2960
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In this paper, we propose a novel graph-based semisupervised learning framework, called joint sparse representation and embedding propagation learning (JSREPL). The idea of JSREPL is to join EPL with sparse representation to perform label propagation. Like most of graph-based semisupervised propagation learning algorithms, JSREPL also constructs weights graph matrix from given data. Different from... View full abstract»

• ### The Twist Tensor Nuclear Norm for Video Completion

Publication Year: 2017, Page(s):2961 - 2973
Cited by:  Papers (2)
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In this paper, we propose a new low-rank tensor model based on the circulant algebra, namely, twist tensor nuclear norm (t-TNN). The twist tensor denotes a three-way tensor representation to laterally store 2-D data slices in order. On one hand, t-TNN convexly relaxes the tensor multirank of the twist tensor in the Fourier domain, which allows an efficient computation using fast Fourier transform.... View full abstract»

• ### Delay-Dependent Global Exponential Stability for Delayed Recurrent Neural Networks

Publication Year: 2017, Page(s):2974 - 2984
Cited by:  Papers (6)
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This paper deals with the global exponential stability for delayed recurrent neural networks (DRNNs). By constructing an augmented Lyapunov-Krasovskii functional and adopting the reciprocally convex combination approach and Wirtinger-based integral inequality, delay-dependent global exponential stability criteria are derived in terms of linear matrix inequalities. Meanwhile, a general and effectiv... View full abstract»

• ### Assessing Generalization Ability of Majority Vote Point Classifiers

Publication Year: 2017, Page(s):2985 - 2997
Cited by:  Papers (3)
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Classification algorithms have been traditionally designed to simultaneously reduce errors caused by bias as well by variance. However, there occur many situations where low generalization error becomes extremely crucial to getting tangible classification solutions, and even slight overfitting causes serious consequences in the test results. In such situations, classifiers with low Vapnik-Chervone... View full abstract»

• ### Distributed Finite-Time Cooperative Control of Multiple High-Order Nonholonomic Mobile Robots

Publication Year: 2017, Page(s):2998 - 3006
Cited by:  Papers (11)
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The consensus problem of multiple nonholonomic mobile robots in the form of high-order chained structure is considered in this paper. Based on the model features and the finite-time control technique, a finite-time cooperative controller is explicitly constructed which guarantees that the states consensus is achieved in a finite time. As an application of the proposed results, finite-time formatio... View full abstract»

• ### Method for Determining the Optimal Number of Clusters Based on Agglomerative Hierarchical Clustering

Publication Year: 2017, Page(s):3007 - 3017
Cited by:  Papers (1)
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It is crucial to determine the optimal number of clusters for the clustering quality in cluster analysis. From the standpoint of sample geometry, two concepts, i.e., the sample clustering dispersion degree and the sample clustering synthesis degree, are defined, and a new clustering validity index is designed. Moreover, a method for determining the optimal number of clusters based on an agglomerat... View full abstract»

• ### An Improved Result on Dissipativity and Passivity Analysis of Markovian Jump Stochastic Neural Networks With Two Delay Components

Publication Year: 2017, Page(s):3018 - 3031
Cited by:  Papers (2)
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In this paper, we investigate the dissipativity and passivity of Markovian jump stochastic neural networks involving two additive time-varying delays. Using a Lyapunov-Krasovskii functional with triple and quadruple integral terms, we obtain delay-dependent passivity and dissipativity criteria for the system. Using a generalized Finsler lemma (GFL), a set of slack variables with special structure ... View full abstract»

• ### A Novel Unified and Self-Stabilizing Algorithm for Generalized Eigenpairs Extraction

Publication Year: 2017, Page(s):3032 - 3044
Cited by:  Papers (1)
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Generalized eigendecomposition problem has been widely employed in many signal processing applications. In this paper, we propose a unified and self-stabilizing algorithm, which is able to extract the first principal and minor generalized eigenvectors of a matrix pencil of two vector sequences adaptively. Furthermore, we extend the proposed algorithm to extract multiple generalized eigenvectors. T... View full abstract»

• ### Evolutionary Cost-Sensitive Extreme Learning Machine

Publication Year: 2017, Page(s):3045 - 3060
Cited by:  Papers (4)
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Conventional extreme learning machines (ELMs) solve a Moore-Penrose generalized inverse of hidden layer activated matrix and analytically determine the output weights to achieve generalized performance, by assuming the same loss from different types of misclassification. The assumption may not hold in cost-sensitive recognition tasks, such as face recognition-based access control system, where mis... View full abstract»

• ### Novel Formulation of Adaptive MPC as EKF Using ANN Model: Multiproduct Semibatch Polymerization Reactor Case Study

Publication Year: 2017, Page(s):3061 - 3073
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In this paper, a novel formulation for nonlinear model predictive control (MPC) has been proposed incorporating the extended Kalman filter (EKF) control concept using a purely data-driven artificial neural network (ANN) model based on measurements for supervisory control. The proposed scheme consists of two modules focusing on online parameter estimation based on past measurements and control esti... View full abstract»

• ### A New Neural Dynamic Classification Algorithm

Publication Year: 2017, Page(s):3074 - 3083
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The keys for the development of an effective classification algorithm are: 1) discovering feature spaces with large margins between clusters and close proximity of the classmates and 2) discovering the smallest number of the features to perform accurate classification. In this paper, a new supervised classification algorithm, called neural dynamic classification (NDC), is presented with the goal o... View full abstract»

• ### Integration of Semantic and Episodic Memories

Publication Year: 2017, Page(s):3084 - 3095
Cited by:  Papers (3)
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This paper describes the integration of semantic and episodic memory (EM) models and the benefits of such integration. Semantic memory (SM) is used as a foundation of knowledge and concept learning, and is needed for the operation of any cognitive system. EM retains personal experiences stored based on their significance-it is supported by the SM, and in return, it supports SM operations. Integrat... View full abstract»

• ### A New Result on $H_{\infty }$ State Estimation of Delayed Static Neural Networks

Publication Year: 2017, Page(s):3096 - 3101
Cited by:  Papers (3)
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This brief presents a new guaranteed H performance state estimation criterion for delayed static neural networks. To facilitate the use of the slope information about activation function, the estimation error of activation function is separated into two parts for the first time. Then, a novel Lyapunov-Krasovskii functional (LKF) is constructed, which has fully captured the slope information of the... View full abstract»

• ### Underdetermined Blind Source Separation Using Sparse Coding

Publication Year: 2017, Page(s):3102 - 3108
Cited by:  Papers (4)
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In an underdetermined mixture system with n unknown sources, it is a challenging task to separate these sources from their m observed mixture signals, where m . n. By exploiting the technique of sparse coding, we propose an effective approach to discover some 1-D subspaces from the set consisting of all the time-frequency (TF) representation vectors of observed mixture signals. We show that these ... View full abstract»

• ### 2017 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 28

Publication Year: 2017, Page(s):1 - 37
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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