# IEEE Transactions on Neural Networks and Learning Systems

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Displaying Results 1 - 23 of 23

Publication Year: 2016, Page(s): C1
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• ### IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Publication Year: 2016, Page(s): C2
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• ### Probe Machine

Publication Year: 2016, Page(s):1405 - 1416
Cited by:  Papers (19)
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In this paper, we present a novel computing model, called probe machine (PM). Unlike the turing machine (TM), PM is a fully parallel computing model in the sense that it can simultaneously process multiple pairs of data, rather than sequentially process every pair of linearly adjacent data. We establish the mathematical model of PM as a nine-tuple consisting of data library, probe library, data co... View full abstract»

• ### Learning Compositional Shape Models of Multiple Distance Metrics by Information Projection

Publication Year: 2016, Page(s):1417 - 1428
Cited by:  Papers (1)
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This paper presents a novel compositional contour-based shape model by incorporating multiple distance metrics to account for varying shape distortions or deformations. Our approach contains two key steps: 1) contour feature generation and 2) generative model pursuit. For each category, we first densely sample an ensemble of local prototype contour segments from a few positive shape examples and d... View full abstract»

• ### Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery

Publication Year: 2016, Page(s):1429 - 1444
Cited by:  Papers (3)
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In this paper we first point out a fatal drawback that the widely used Granger causality (GC) needs to estimate the autoregressive model, which is equivalent to taking a series of backward recursive operations which are infeasible in many irreversible chemical reaction models. Thus, new causality (NC) proposed by Hu et al. (2011) is theoretically shown to be more sensitive to reveal true causality... View full abstract»

• ### Alternative Multiview Maximum Entropy Discrimination

Publication Year: 2016, Page(s):1445 - 1456
Cited by:  Papers (2)
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Maximum entropy discrimination (MED) is a general framework for discriminative estimation based on maximum entropy and maximum margin principles, and can produce hard-margin support vector machines under some assumptions. Recently, the multiview version of MED multiview MED (MVMED) was proposed. In this paper, we try to explore a more natural MVMED framework by assuming two separate distributions ... View full abstract»

• ### Parallel Online Temporal Difference Learning for Motor Control

Publication Year: 2016, Page(s):1457 - 1468
Cited by:  Papers (3)
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Temporal difference (TD) learning, a key concept in reinforcement learning, is a popular method for solving simulated control problems. However, in real systems, this method is often avoided in favor of policy search methods because of its long learning time. But policy search suffers from its own drawbacks, such as the necessity of informed policy parameterization and initialization. In this pape... View full abstract»

• ### Sparse Uncorrelated Linear Discriminant Analysis for Undersampled Problems

Publication Year: 2016, Page(s):1469 - 1485
Cited by:  Papers (2)
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Linear discriminant analysis (LDA) as a well-known supervised dimensionality reduction method has been widely applied in many fields. However, the lack of sparsity in the LDA solution makes interpretation of the results challenging. In this paper, we propose a new model for sparse uncorrelated LDA (ULDA). Our model is based on the characterization of all solutions of the generalized ULDA. We incor... View full abstract»

• ### Stability Analysis for Delayed Neural Networks Considering Both Conservativeness and Complexity

Publication Year: 2016, Page(s):1486 - 1501
Cited by:  Papers (33)
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This paper investigates delay-dependent stability for continuous neural networks with a time-varying delay. This paper aims at deriving a new stability criterion, considering tradeoff between conservativeness and calculation complexity. A new Lyapunov-Krasovskii functional with simple augmented terms and delay-dependent terms is constructed, and its derivative is estimated by several techniques, i... View full abstract»

• ### Compound Rank- $k$ Projections for Bilinear Analysis

Publication Year: 2016, Page(s):1502 - 1513
Cited by:  Papers (32)
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In many real-world applications, data are represented by matrices or high-order tensors. Despite the promising performance, the existing 2-D discriminant analysis algorithms employ a single projection model to exploit the discriminant information for projection, making the model less flexible. In this paper, we propose a novel compound rank-k projection (CRP) algorithm for bilinear analysis. The C... View full abstract»

• ### Constrained Clustering With Nonnegative Matrix Factorization

Publication Year: 2016, Page(s):1514 - 1526
Cited by:  Papers (2)
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Nonnegative matrix factorization (NMF) and symmetric NMF (SymNMF) have been shown to be effective for clustering linearly separable data and nonlinearly separable data, respectively. Nevertheless, many practical applications demand constrained algorithms in which a small number of constraints in the form of must-link and cannot-link are available. In this paper, we propose an NMF-based constrained... View full abstract»

• ### Control of Large-Scale Boolean Networks via Network Aggregation

Publication Year: 2016, Page(s):1527 - 1536
Cited by:  Papers (5)
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A major challenge to solve problems in control of Boolean networks is that the computational cost increases exponentially when the number of nodes in the network increases. We consider the problem of controllability and stabilizability of Boolean control networks, address the increasing cost problem by partitioning the network graph into several subnetworks, and analyze the subnetworks separately.... View full abstract»

• ### Near-Optimal Controller for Nonlinear Continuous-Time Systems With Unknown Dynamics Using Policy Iteration

Publication Year: 2016, Page(s):1537 - 1549
Cited by:  Papers (2)
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This paper presents a single-network adaptive critic-based controller for continuous-time systems with unknown dynamics in a policy iteration (PI) framework. It is assumed that the unknown dynamics can be estimated using the Takagi-Sugeno-Kang fuzzy model with arbitrary precision. The successful implementation of a PI scheme depends on the effective learning of critic network parameters. Network p... View full abstract»

• ### Image Super-Resolution via Adaptive $ell _{p} (0<p<1)$ Regularization and Sparse Representation

Publication Year: 2016, Page(s):1550 - 1561
Cited by:  Papers (5)
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Previous studies have shown that image patches can be well represented as a sparse linear combination of elements from an appropriately selected over-complete dictionary. Recently, single-image super-resolution (SISR) via sparse representation using blurred and downsampled low-resolution images has attracted increasing interest, where the aim is to obtain the coefficients for sparse representation... View full abstract»

• ### Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints

Publication Year: 2016, Page(s):1562 - 1571
Cited by:  Papers (59)
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In order to stabilize a class of uncertain nonlinear strict-feedback systems with full-state constraints, an adaptive neural network control method is investigated in this paper. The state constraints are frequently emerged in the real-life plants and how to avoid the violation of state constraints is an important task. By introducing a barrier Lyapunov function (BLF) to every step in a backsteppi... View full abstract»

• ### Learning Spike Time Codes Through Morphological Learning With Binary Synapses

Publication Year: 2016, Page(s):1572 - 1577
Cited by:  Papers (2)
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In this brief, a neuron with nonlinear dendrites (NNLDs) and binary synapses that is able to learn temporal features of spike input patterns is considered. Since binary synapses are considered, learning happens through formation and elimination of connections between the inputs and the dendritic branches to modify the structure or morphology of the NNLD. A morphological learning algorithm inspired... View full abstract»

• ### Saturated Finite Interval Iterative Learning for Tracking of Dynamic Systems With HNN-Structural Output

Publication Year: 2016, Page(s):1578 - 1584
Cited by:  Papers (4)
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This brief investigates the interval iterative learning problem for dynamic systems with hierarchical neural network (HNN)-structural output. The first objective is to design the output of a dynamic system with HNN structure. A sufficient condition is obtained to achieve the interval tracking in a finite interval by applying iterative learning control (ILC). Then, the saturated ILC is considered i... View full abstract»

• ### Pinning Control Design for the Stabilization of Boolean Networks

Publication Year: 2016, Page(s):1585 - 1590
Cited by:  Papers (21)
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In this brief, pinning control design for the stabilization of Boolean network (BN) is addressed. Using the semitensor product of matrices, transition matrix of the BN can be obtained. We achieve global stability to the fixed point or the elementary cycle for the BN by changing the columns of the transition matrix. Then, pinning nodes can be chosen, and pinning control design algorithms are propos... View full abstract»

• ### Can the Virtual Labels Obtained by Traditional LP Approaches Be Well Encoded in WLR?

Publication Year: 2016, Page(s):1591 - 1598
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Semisupervised dimension reduction via virtual label regression first derives the virtual labels of unlabeled data by employing a newly designed label propagation (LP) approach (called Special random walk (SRW)) and then encodes them in a weighted linear regression model. Nie et al. (2011) highlighted two important characteristics of SRW nonexistent in the previous LP approaches: outlier detection... View full abstract»

• ### Call For Papers: IEEE Transactions on Cognitive and Developmental Systems

Publication Year: 2016, Page(s): 1599
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• ### Call For Papers: IEEE World Congress on Computational Intelligence

Publication Year: 2016, Page(s): 1600
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• ### IEEE Computational Intelligence Society Information

Publication Year: 2016, Page(s): C3
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• ### IEEE Transactions on Neural Networks information for authors

Publication Year: 2016, 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