# IEEE Transactions on Neural Networks and Learning Systems

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Displaying Results 1 - 25 of 26

Publication Year: 2015, Page(s):C1 - 1349
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• ### IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Publication Year: 2015, Page(s): C2
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• ### Adaptive Output-Feedback Neural Control of Switched Uncertain Nonlinear Systems With Average Dwell Time

Publication Year: 2015, Page(s):1350 - 1362
Cited by:  Papers (47)
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This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by neural networks. A novel adaptive neural control technique for the problem studied is set up by exploiting the average dwell time method and backsteppin... View full abstract»

• ### A Neurodynamic Optimization Method for Recovery of Compressive Sensed Signals With Globally Converged Solution Approximating to $l_{0}$ Minimization

Publication Year: 2015, Page(s):1363 - 1374
Cited by:  Papers (2)
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Finding the optimal solution to the constrained l0-norm minimization problems in the recovery of compressive sensed signals is an NP-hard problem and it usually requires intractable combinatorial searching operations for getting the global optimal solution, unless using other objective functions (e.g., the l1 norm or l p norm) for approximate solutions or using greedy search ... View full abstract»

• ### Nonlinear Topological Component Analysis: Application to Age-Invariant Face Recognition

Publication Year: 2015, Page(s):1375 - 1387
Cited by:  Papers (4)
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We introduce a novel formalism that performs dimensionality reduction and captures topological features (such as the shape of the observed data) to conduct pattern classification. This mission is achieved by: 1) reducing the dimension of the observed variables through a kernelized radial basis function technique and expressing the latent variables probability distribution in terms of the observed ... View full abstract»

• ### FREL: A Stable Feature Selection Algorithm

Publication Year: 2015, Page(s):1388 - 1402
Cited by:  Papers (7)
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Two factors characterize a good feature selection algorithm: its accuracy and stability. This paper aims at introducing a new approach to stable feature selection algorithms. The innovation of this paper centers on a class of stable feature selection algorithms called feature weighting as regularized energy-based learning (FREL). Stability properties of FREL using L1 or L2 regularization are inves... View full abstract»

• ### Incremental Support Vector Learning for Ordinal Regression

Publication Year: 2015, Page(s):1403 - 1416
Cited by:  Papers (330)
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Support vector ordinal regression (SVOR) is a popular method to tackle ordinal regression problems. However, until now there were no effective algorithms proposed to address incremental SVOR learning due to the complicated formulations of SVOR. Recently, an interesting accurate on-line algorithm was proposed for training ν-support vector classification (ν-SVC), which can handle a qua... View full abstract»

• ### On Equivalence of FIS and ELM for Interpretable Rule-Based Knowledge Representation

Publication Year: 2015, Page(s):1417 - 1430
Cited by:  Papers (12)
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This paper presents a fuzzy extreme learning machine (F-ELM) that embeds fuzzy membership functions and rules into the hidden layer of extreme learning machine (ELM). Similar to the concept of ELM that employed the random initialization technique, three parameters of F-ELM are randomly assigned. They are the standard deviation of the membership functions, matrix-C (rule-combination matrix), and ma... View full abstract»

• ### Exponential Stabilization of Memristor-based Chaotic Neural Networks with Time-Varying Delays via Intermittent Control

Publication Year: 2015, Page(s):1431 - 1441
Cited by:  Papers (37)
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This paper is concerned with the global exponential stabilization of memristor-based chaotic neural networks with both time-varying delays and general activation functions. Here, we adopt nonsmooth analysis and control theory to handle memristor-based chaotic neural networks with discontinuous right-hand side. In particular, several new sufficient conditions ensuring exponential stabilization of m... View full abstract»

• ### An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination

Publication Year: 2015, Page(s):1442 - 1455
Cited by:  Papers (7)
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We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but al... View full abstract»

• ### Finite-Horizon Approximate Optimal Guaranteed Cost Control of Uncertain Nonlinear Systems With Application to Mars Entry Guidance

Publication Year: 2015, Page(s):1456 - 1467
Cited by:  Papers (5)
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This paper studies the finite-horizon optimal guaranteed cost control (GCC) problem for a class of time-varying uncertain nonlinear systems. The aim of this problem is to find a robust state feedback controller such that the closed-loop system has not only a bounded response in a finite duration of time for all admissible uncertainties but also a minimal guaranteed cost. A neural network (NN) base... View full abstract»

• ### Multitask Classification Hypothesis Space With Improved Generalization Bounds

Publication Year: 2015, Page(s):1468 - 1479
Cited by:  Papers (1)
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This paper presents a pair of hypothesis spaces (HSs) of vector-valued functions intended to be used in the context of multitask classification. While both are parameterized on the elements of reproducing kernel Hilbert spaces and impose a feature mapping that is common to all tasks, one of them assumes this mapping as fixed, while the more general one learns the mapping via multiple kernel learni... View full abstract»

• ### Stability Analysis of Distributed Delay Neural Networks Based on Relaxed Lyapunov–Krasovskii Functionals

Publication Year: 2015, Page(s):1480 - 1492
Cited by:  Papers (41)
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This paper revisits the problem of asymptotic stability analysis for neural networks with distributed delays. The distributed delays are assumed to be constant and prescribed. Since a positive-definite quadratic functional does not necessarily require all the involved symmetric matrices to be positive definite, it is important for constructing relaxed Lyapunov-Krasovskii functionals, which general... View full abstract»

• ### Lag Synchronization of Switched Neural Networks via Neural Activation Function and Applications in Image Encryption

Publication Year: 2015, Page(s):1493 - 1502
Cited by:  Papers (44)
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This paper investigates the problem of global exponential lag synchronization of a class of switched neural networks with time-varying delays via neural activation function and applications in image encryption. The controller is dependent on the output of the system in the case of packed circuits, since it is hard to measure the inner state of the circuits. Thus, it is critical to design the contr... View full abstract»

• ### Dynamic Uncertain Causality Graph for Knowledge Representation and Probabilistic Reasoning: Directed Cyclic Graph and Joint Probability Distribution

Publication Year: 2015, Page(s):1503 - 1517
Cited by:  Papers (6)
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Probabilistic graphical models (PGMs) such as Bayesian network (BN) have been widely applied in uncertain causality representation and probabilistic reasoning. Dynamic uncertain causality graph (DUCG) is a newly presented model of PGMs, which can be applied to fault diagnosis of large and complex industrial systems, disease diagnosis, and so on. The basic methodology of DUCG has been previously pr... View full abstract»

• ### The Connection Between Bayesian Estimation of a Gaussian Random Field and RKHS

Publication Year: 2015, Page(s):1518 - 1524
Cited by:  Papers (2)
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Reconstruction of a function from noisy data is key in machine learning and is often formulated as a regularized optimization problem over an infinite-dimensional reproducing kernel Hilbert space (RKHS). The solution suitably balances adherence to the observed data and the corresponding RKHS norm. When the data fit is measured using a quadratic loss, this estimator has a known statistical interpre... View full abstract»

• ### Discrete-Time Zhang Neural Network for Online Time-Varying Nonlinear Optimization With Application to Manipulator Motion Generation

Publication Year: 2015, Page(s):1525 - 1531
Cited by:  Papers (18)
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In this brief, a discrete-time Zhang neural network (DTZNN) model is first proposed, developed, and investigated for online time-varying nonlinear optimization (OTVNO). Then, Newton iteration is shown to be derived from the proposed DTZNN model. In addition, to eliminate the explicit matrix-inversion operation, the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is introduced, which ca... View full abstract»

• ### Adaptive NN Control of a Class of Nonlinear Systems With Asymmetric Saturation Actuators

Publication Year: 2015, Page(s):1532 - 1538
Cited by:  Papers (29)
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In this note, adaptive neural network (NN) control is investigated for a class of uncertain nonlinear systems with asymmetric saturation actuators and external disturbances. To handle the effect of nonsmooth asymmetric saturation nonlinearity, a Gaussian error function-based continuous differentiable asymmetric saturation model is employed such that the backstepping technique can be used in the co... View full abstract»

• ### Phase Oscillatory Network and Visual Pattern Recognition

Publication Year: 2015, Page(s):1539 - 1544
Cited by:  Papers (7)
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We explore a properly interconnected set of Kuramoto type oscillators that results in a new associative-memory network configuration, which includes second- and third-order additional terms in the Fourier expansion of the network's coupling. Investigation of the response of the network to different external stimuli indicates an increase in the network capability for coding and information retrieva... View full abstract»

• ### Optoelectronic Systems Trained With Backpropagation Through Time

Publication Year: 2015, Page(s):1545 - 1550
Cited by:  Papers (3)
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Delay-coupled optoelectronic systems form promising candidates to act as powerful information processing devices. In this brief, we consider such a system that has been studied before in the context of reservoir computing (RC). Instead of viewing the system as a random dynamical system, we see it as a true machine-learning model, which can be fully optimized. We use a recently introduced extension... View full abstract»

• ### Ordinal Distance Metric Learning for Image Ranking

Publication Year: 2015, Page(s):1551 - 1559
Cited by:  Papers (8)
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Recently, distance metric learning (DML) has attracted much attention in image retrieval, but most previous methods only work for image classification and clustering tasks. In this brief, we focus on designing ordinal DML algorithms for image ranking tasks, by which the rank levels among the images can be well measured. We first present a linear ordinal Mahalanobis DML model that tries to preserve... View full abstract»

• ### Neural Feedback Passivity of Unknown Nonlinear Systems via Sliding Mode Technique

Publication Year: 2015, Page(s):1560 - 1566
Cited by:  Papers (2)
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Passivity method is very effective to analyze large-scale nonlinear systems with strong nonlinearities. However, when most parts of the nonlinear system are unknown, the published neural passivity methods are not suitable for feedback stability. In this brief, we propose a novel sliding mode learning algorithm and sliding mode feedback passivity control. We prove that for a wide class of unknown n... View full abstract»

• ### A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study

Publication Year: 2015, Page(s):1567 - 1574
Cited by:  Papers (11)
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Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain cond... View full abstract»

• ### A Deterministic Analysis of an Online Convex Mixture of Experts Algorithm

Publication Year: 2015, Page(s):1575 - 1580
Cited by:  Papers (3)
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We analyze an online learning algorithm that adaptively combines outputs of two constituent algorithms (or the experts) running in parallel to estimate an unknown desired signal. This online learning algorithm is shown to achieve and in some cases outperform the mean-square error (MSE) performance of the best constituent algorithm in the steady state. However, the MSE analysis of this algorithm in... View full abstract»

• ### IEEE Computational Intelligence Society Information

Publication Year: 2015, Page(s): C3
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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