# IEEE Transactions on Neural Networks and Learning Systems

## Filter Results

Displaying Results 1 - 21 of 21

Publication Year: 2016, Page(s): C1
| PDF (122 KB)
• ### IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Publication Year: 2016, Page(s): C2
| PDF (141 KB)
• ### Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems

Publication Year: 2016, Page(s):497 - 509
Cited by:  Papers (31)
| | PDF (1666 KB) | HTML

This paper presents a novel approximation-based event-triggered control of multi-input multi-output uncertain nonlinear continuous-time systems in affine form. The controller is approximated using a linearly parameterized neural network (NN) in the context of event-based sampling. After revisiting the NN approximation property in the context of event-based sampling, an event-triggered condition is... View full abstract»

• ### Robust Adaptive Neural Tracking Control for a Class of Stochastic Nonlinear Interconnected Systems

Publication Year: 2016, Page(s):510 - 523
Cited by:  Papers (48)
| | PDF (1681 KB) | HTML

In this paper, an adaptive neural decentralized control approach is proposed for a class of multiple input and multiple output uncertain stochastic nonlinear strong interconnected systems. Radial basis function neural networks are used to approximate the packaged unknown nonlinearities, and backstepping technique is utilized to construct an adaptive neural decentralized controller. The proposed co... View full abstract»

• ### Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models

Publication Year: 2016, Page(s):524 - 537
Cited by:  Papers (20)
| | PDF (2885 KB) | HTML

Automatic Web-service selection is an important research topic in the domain of service computing. During this process, reliable predictions for quality of service (QoS) based on historical service invocations are vital to users. This work aims at making highly accurate predictions for missing QoS data via building an ensemble of nonnegative latent factor (NLF) models. Its motivations are: 1) the ... View full abstract»

• ### Comparison of Seven Methods for Boolean Factor Analysis and Their Evaluation by Information Gain

Publication Year: 2016, Page(s):538 - 550
Cited by:  Papers (2)
| | PDF (1451 KB) | HTML

An usual task in large data set analysis is searching for an appropriate data representation in a space of fewer dimensions. One of the most efficient methods to solve this task is factor analysis. In this paper, we compare seven methods for Boolean factor analysis (BFA) in solving the so-called bars problem (BP), which is a BFA benchmark. The performance of the methods is evaluated by means of in... View full abstract»

• ### A New Stochastic Computing Methodology for Efficient Neural Network Implementation

Publication Year: 2016, Page(s):551 - 564
Cited by:  Papers (9)
| | PDF (4450 KB) | HTML

This paper presents a new methodology for the hardware implementation of neural networks (NNs) based on probabilistic laws. The proposed encoding scheme circumvents the limitations of classical stochastic computing (based on unipolar or bipolar encoding) extending the representation range to any real number using the ratio of two bipolar-encoded pulsed signals. Furthermore, the novel approach pres... View full abstract»

• ### Hierarchical Theme and Topic Modeling

Publication Year: 2016, Page(s):565 - 578
Cited by:  Papers (2)
| | PDF (2717 KB) | HTML

Considering the hierarchical data groupings in text corpus, e.g., words, sentences, and documents, we conduct the structural learning and infer the latent themes and topics for sentences and words from a collection of documents, respectively. The relation between themes and topics under different data groupings is explored through an unsupervised procedure without limiting the number of clusters. ... View full abstract»

• ### A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method

Publication Year: 2016, Page(s):579 - 592
Cited by:  Papers (17)
| | PDF (3841 KB) | HTML

Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a target matrix, which is critically important in collaborative filtering (CF)-based recommender systems. However, current NMF-based CF recommenders suffer from the problem of high computational and storage complexity, as well as slow convergence rate, which prevents them from industrial usage in context of big ... View full abstract»

• ### Global Exponential Stability for Complex-Valued Recurrent Neural Networks With Asynchronous Time Delays

Publication Year: 2016, Page(s):593 - 606
Cited by:  Papers (28)
| | PDF (1303 KB) | HTML

In this paper, we investigate the global exponential stability for complex-valued recurrent neural networks with asynchronous time delays by decomposing complex-valued networks to real and imaginary parts and construct an equivalent real-valued system. The network model is described by a continuous-time equation. There are two main differences of this paper with previous works: 1) time delays can ... View full abstract»

• ### Perception Evolution Network Based on Cognition Deepening Model—Adapting to the Emergence of New Sensory Receptor

Publication Year: 2016, Page(s):607 - 620
Cited by:  Papers (1)
| | PDF (3079 KB) | HTML

The proposed perception evolution network (PEN) is a biologically inspired neural network model for unsupervised learning and online incremental learning. It is able to automatically learn suitable prototypes from learning data in an incremental way, and it does not require the predefined prototype number or the predefined similarity threshold. Meanwhile, being more advanced than the existing unsu... View full abstract»

• ### A Spiking Neural Network System for Robust Sequence Recognition

Publication Year: 2016, Page(s):621 - 635
Cited by:  Papers (5)
| | PDF (3883 KB) | HTML

This paper proposes a biologically plausible network architecture with spiking neurons for sequence recognition. This architecture is a unified and consistent system with functional parts of sensory encoding, learning, and decoding. This is the first systematic model attempting to reveal the neural mechanisms considering both the upstream and the downstream neurons together. The whole system is a ... View full abstract»

• ### DC Proximal Newton for Nonconvex Optimization Problems

Publication Year: 2016, Page(s):636 - 647
Cited by:  Papers (3)
| | PDF (1059 KB) | HTML

We introduce a novel algorithm for solving learning problems where both the loss function and the regularizer are nonconvex but belong to the class of difference of convex (DC) functions. Our contribution is a new general purpose proximal Newton algorithm that is able to deal with such a situation. The algorithm consists in obtaining a descent direction from an approximation of the loss function a... View full abstract»

• ### Relevance Vector Machine for Survival Analysis

Publication Year: 2016, Page(s):648 - 660
Cited by:  Papers (3)
| | PDF (3074 KB) | HTML

An accelerated failure time (AFT) model has been widely used for the analysis of censored survival or failure time data. However, the AFT imposes the restrictive log-linear relation between the survival time and the explanatory variables. In this paper, we introduce a relevance vector machine survival (RVMS) model based on Weibull AFT model that enables the use of kernel framework to automatically... View full abstract»

• ### Analog Programmable Distance Calculation Circuit for Winner Takes All Neural Network Realized in the CMOS Technology

Publication Year: 2016, Page(s):661 - 673
Cited by:  Papers (5)
| | PDF (3296 KB) | HTML

This paper presents a programmable analog current-mode circuit used to calculate the distance between two vectors of currents, following two distance measures. The Euclidean (L2) distance is commonly used. However, in many situations, it can be replaced with the Manhattan (L1) one, which is computationally less intensive, whose realization comes with less power dissipation and lower hardware compl... View full abstract»

• ### Image Categorization by Learning a Propagated Graphlet Path

Publication Year: 2016, Page(s):674 - 685
Cited by:  Papers (8)
| | PDF (4755 KB) | HTML

Spatial pyramid matching is a standard architecture for categorical image retrieval. However, its performance is largely limited by the prespecified rectangular spatial regions when pooling local descriptors. In this paper, we propose to learn object-shaped and directional receptive fields for image categorization. In particular, different objects in an image are seamlessly constructed by superpix... View full abstract»

• ### Lag Synchronization of Memristor-Based Coupled Neural Networks via $omega$ -Measure

Publication Year: 2016, Page(s):686 - 697
Cited by:  Papers (11)
| | PDF (3974 KB) | HTML

This paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the ω -... View full abstract»

• ### $L_{1}$ -Minimization Algorithms for Sparse Signal Reconstruction Based on a Projection Neural Network

Publication Year: 2016, Page(s):698 - 707
Cited by:  Papers (10)
| | PDF (1525 KB) | HTML

This paper presents several L1 -minimization algorithms for sparse signal reconstruction based on a continuous-time projection neural network (PNN). First, a one-layer projection neural network is designed based on a projection operator and a projection matrix. The stability and global convergence of the proposed neural network are proved. Then, based on a discrete-time version of the P... View full abstract»

• ### IEEE Transactions on Cognitive and Developmental Systems

Publication Year: 2016, Page(s): 708
| PDF (533 KB)
• ### IEEE Computational Intelligence Society Information

Publication Year: 2016, Page(s): C3
| PDF (119 KB)
• ### IEEE Transactions on Neural Networks information for authors

Publication Year: 2016, Page(s): C4
| PDF (46 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.

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