# IEEE Transactions on Neural Networks and Learning Systems

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• ### Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture

Publication Year: 2018, Page(s):10 - 24
Cited by:  Papers (3)
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Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is establis... View full abstract»

• ### LSTM: A Search Space Odyssey

Publication Year: 2017, Page(s):2222 - 2232
Cited by:  Papers (12)
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Several variants of the long short-term memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks have become the state-of-the-art models for a variety of machine learning problems. This has led to a renewed interest in understanding the role and utility of various computational components of typical LSTM variants. In t... View full abstract»

• ### Extreme Learning Machine for Multilayer Perceptron

Publication Year: 2016, Page(s):809 - 821
Cited by:  Papers (105)
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Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number... View full abstract»

• ### Deep Direct Reinforcement Learning for Financial Signal Representation and Trading

Publication Year: 2017, Page(s):653 - 664
Cited by:  Papers (2)
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Can we train the computer to beat experienced traders for financial assert trading? In this paper, we try to address this challenge by introducing a recurrent deep neural network (NN) for real-time financial signal representation and trading. Our model is inspired by two biological-related learning concepts of deep learning (DL) and reinforcement learning (RL). In the framework, the DL part automa... View full abstract»

• ### Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene

Publication Year: 2017, Page(s):690 - 703
Cited by:  Papers (7)
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Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for succe... View full abstract»

• ### Optimal and Autonomous Control Using Reinforcement Learning: A Survey

Publication Year: 2018, Page(s):2042 - 2062
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This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal H2 and H∞ control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and ... View full abstract»

• ### Convolution in Convolution for Network in Network

Publication Year: 2018, Page(s):1587 - 1597
Cited by:  Papers (1)
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Network in network (NiN) is an effective instance and an important extension of deep convolutional neural network consisting of alternating convolutional layers and pooling layers. Instead of using a linear filter for convolution, NiN utilizes shallow multilayer perceptron (MLP), a nonlinear function, to replace the linear filter. Because of the powerfulness of MLP and 1 × 1 convolutions in... View full abstract»

• ### Efficient kNN Classification With Different Numbers of Nearest Neighbors

Publication Year: 2018, Page(s):1774 - 1785
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k nearest neighbor (kNN) method is a popular classification method in data mining and statistics because of its simple implementation and significant classification performance. However, it is impractical for traditional kNN methods to assign a fixed k value (even though set by experts) to all test samples. Previous solutions assign different k values to different test samples by the cross validat... View full abstract»

• ### Applications of Deep Learning and Reinforcement Learning to Biological Data

Publication Year: 2018, Page(s):2063 - 2079
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Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent re... View full abstract»

• ### Evaluating the Visualization of What a Deep Neural Network Has Learned

Publication Year: 2017, Page(s):2660 - 2673
Cited by:  Papers (3)
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Deep neural networks (DNNs) have demonstrated impressive performance in complex machine learning tasks such as image classification or speech recognition. However, due to their multilayer nonlinear structure, they are not transparent, i.e., it is hard to grasp what makes them arrive at a particular classification or recognition decision, given a new unseen data sample. Recently, several approaches... View full abstract»

• ### Detecting Wash Trade in Financial Market Using Digraphs and Dynamic Programming

Publication Year: 2016, Page(s):2351 - 2363
Cited by:  Papers (2)
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A wash trade refers to the illegal activities of traders who utilize carefully designed limit orders to manually increase the trading volumes for creating a false impression of an active market. As one of the primary formats of market abuse, a wash trade can be extremely damaging to the proper functioning and integrity of capital markets. The existing work focuses on collusive clique detections ba... View full abstract»

• ### A Survey of Memristive Threshold Logic Circuits

Publication Year: 2017, Page(s):1734 - 1746
Cited by:  Papers (1)
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In this paper, we review different memristive threshold logic (MTL) circuits that are inspired from the synaptic action of the flow of neurotransmitters in the biological brain. The brainlike generalization ability and the area minimization of these threshold logic circuits aim toward crossing Moore's law boundaries at device, circuits, and systems levels. Fast switching memory, signal processing,... View full abstract»

• ### Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks

Publication Year: 2016, Page(s):125 - 138
Cited by:  Papers (30)
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This paper presents a novel change detection approach for synthetic aperture radar images based on deep learning. The approach accomplishes the detection of the changed and unchanged areas by designing a deep neural network. The main guideline is to produce a change detection map directly from two images with the trained deep neural network. The method can omit the process of generating a differen... View full abstract»

• ### Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks

Publication Year: 2018, Page(s):681 - 694
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In this paper, a variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenge of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate, which influences the perform... View full abstract»

• ### DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks

Publication Year: 2018, Page(s):1441 - 1453
| | PDF (1995 KB) | HTML

Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limi... View full abstract»

• ### A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control

Publication Year: 2016, Page(s):416 - 425
Cited by:  Papers (230)
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This paper investigates the multirate networked industrial process control problem in double-layer architecture. First, the output tracking problem for sampled-data nonlinear plant at device layer with sampling period Td is investigated using adaptive neural network (NN) control, and it is shown that the outputs of subsystems at device layer can track the decomposed setpoints. Then, the... View full abstract»

• ### Kernel-Based Multilayer Extreme Learning Machines for Representation Learning

Publication Year: 2018, Page(s):757 - 762
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Recently, multilayer extreme learning machine (ML-ELM) was applied to stacked autoencoder (SAE) for representation learning. In contrast to traditional SAE, the training time of ML-ELM is significantly reduced from hours to seconds with high accuracy. However, ML-ELM suffers from several drawbacks: 1) manual tuning on the number of hidden nodes in every layer is an uncertain factor to training tim... View full abstract»

• ### Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data

Publication Year: 2018, Page(s):1301 - 1313
Cited by:  Papers (7)
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In this paper, we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g., object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual qu... View full abstract»

• ### A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images

Publication Year: 2018, Page(s):545 - 559
Cited by:  Papers (3)
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We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. Th... View full abstract»

• ### Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning

Publication Year: 2018, Page(s):1174 - 1186
Cited by:  Papers (6)
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This paper investigates adaptive fuzzy neural network (NN) control using impedance learning for a constrained robot, subject to unknown system dynamics, the effect of state constraints, and the uncertain compliant environment with which the robot comes into contact. A fuzzy NN learning algorithm is developed to identify the uncertain plant model. The prominent feature of the fuzzy NN is that there... View full abstract»

• ### Action-Driven Visual Object Tracking With Deep Reinforcement Learning

Publication Year: 2018, Page(s):2239 - 2252
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In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control tracking actions is pretrained using various training video sequences and fine-tuned during actual tracking for online adaptation to a change of target and... View full abstract»

• ### Preconditioned Stochastic Gradient Descent

Publication Year: 2018, Page(s):1454 - 1466
| | PDF (1955 KB) | HTML

Stochastic gradient descent (SGD) still is the workhorse for many practical problems. However, it converges slow, and can be difficult to tune. It is possible to precondition SGD to accelerate its convergence remarkably. But many attempts in this direction either aim at solving specialized problems, or result in significantly more complicated methods than SGD. This paper proposes a new method to a... View full abstract»

• ### Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals

Publication Year: 2014, Page(s):303 - 315
Cited by:  Papers (88)
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Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load de... View full abstract»

• ### Logistic Localized Modeling of the Sample Space for Feature Selection and Classification

Publication Year: 2018, Page(s):1396 - 1413
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Conventional feature selection algorithms assign a single common feature set to all regions of the sample space. In contrast, this paper proposes a novel algorithm for localized feature selection for which each region of the sample space is characterized by its individual distinct feature subset that may vary in size and membership. This approach can therefore select an optimal feature subset that... View full abstract»

• ### Multivariate Time-Series Classification Using the Hidden-Unit Logistic Model

Publication Year: 2018, Page(s):920 - 931
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We present a new model for multivariate time-series classification, called the hidden-unit logistic model (HULM), that uses binary stochastic hidden units to model latent structure in the data. The hidden units are connected in a chain structure that models temporal dependencies in the data. Compared with the prior models for time-series classification such as the hidden conditional random field, ... View full abstract»

• ### Adaptive Asymptotic Neural Network Control of Nonlinear Systems With Unknown Actuator Quantization

Publication Year: 2018, Page(s):1 - 10
| | PDF (3306 KB)

In this paper, we propose an adaptive neural-network-based asymptotic control algorithm for a class of nonlinear systems subject to unknown actuator quantization. To this end, we exploit the sector property of the quantization nonlinearity and transform actuator quantization control problem into analyzing its upper bounds, which are then handled by a dynamic loop gain function-based approach. In o... View full abstract»

• ### Adaptive Neural Control of Pure-Feedback Nonlinear Systems With Event-Triggered Communications

Publication Year: 2018, Page(s):1 - 10
| | PDF (1546 KB)

This paper is concerned with the adaptive event-triggered control problem for a class of pure-feedback nonlinear systems. Unlike the existing results where the control execution is periodic, the new proposed scheme updates the controller and the neural network weights only when desired control specifications cannot be guaranteed. Clearly, this can largely reduce the amount of transmission data. Be... View full abstract»

• ### Behavioral Learning in a Cognitive Neuromorphic Robot: An Integrative Approach

Publication Year: 2018, Page(s):1 - 13
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We present here a learning system using the iCub humanoid robot and the SpiNNaker neuromorphic chip to solve the real-world task of object-specific attention. Integrating spiking neural networks with robots introduces considerable complexity for questionable benefit if the objective is simply task performance. But, we suggest, in a cognitive robotics context, where the goal is understanding how to... View full abstract»

• ### Tensor-Factorized Neural Networks

Publication Year: 2018, Page(s):1998 - 2011
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The growing interests in multiway data analysis and deep learning have drawn tensor factorization (TF) and neural network (NN) as the crucial topics. Conventionally, the NN model is estimated from a set of one-way observations. Such a vectorized NN is not generalized for learning the representation from multiway observations. The classification performance using vectorized NN is constrained, becau... View full abstract»

• ### Computational Model Based on Neural Network of Visual Cortex for Human Action Recognition

Publication Year: 2018, Page(s):1427 - 1440
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In this paper, we propose a bioinspired model for human action recognition through modeling neural mechanisms of information processing in two visual cortical areas: the primary visual cortex (V1) and the middle temporal cortex (MT) dedicated to motion. This model, named V1-MT, is composed of V1 and MT models (layers) corresponding to their cortical areas, which are built with layered spiking neur... View full abstract»

• ### Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach

Publication Year: 2017, Page(s):2371 - 2381
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Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network a... View full abstract»

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

Publication Year: 2017, Page(s):2911 - 2923
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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»

• ### Classification in the Presence of Label Noise: A Survey

Publication Year: 2014, Page(s):845 - 869
Cited by:  Papers (118)
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Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the ... View full abstract»

• ### Data-Driven Multiagent Systems Consensus Tracking Using Model Free Adaptive Control

Publication Year: 2018, Page(s):1514 - 1524
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This paper investigates the data-driven consensus tracking problem for multiagent systems with both fixed communication topology and switching topology by utilizing a distributed model free adaptive control (MFAC) method. Here, agent's dynamics are described by unknown nonlinear systems and only a subset of followers can access the desired trajectory. The dynamical linearization technique is appli... View full abstract»

• ### Structure Learning for Deep Neural Networks Based on Multiobjective Optimization

Publication Year: 2018, Page(s):2450 - 2463
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This paper focuses on the connecting structure of deep neural networks and proposes a layerwise structure learning method based on multiobjective optimization. A model with better generalization can be obtained by reducing the connecting parameters in deep networks. The aim is to find the optimal structure with high representation ability and better generalization for each layer. Then, the visible... View full abstract»

• ### Adaptive Unsupervised Feature Selection With Structure Regularization

Publication Year: 2018, Page(s):944 - 956
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Feature selection is one of the most important dimension reduction techniques for its efficiency and interpretation. Since practical data in large scale are usually collected without labels, and labeling these data are dramatically expensive and time-consuming, unsupervised feature selection has become a ubiquitous and challenging problem. Without label information, the fundamental problem of unsu... View full abstract»

• ### Discriminative Sparse Neighbor Approximation for Imbalanced Learning

Publication Year: 2018, Page(s):1503 - 1513
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Data imbalance is common in many vision tasks where one or more classes are rare. Without addressing this issue, conventional methods tend to be biased toward the majority class with poor predictive accuracy for the minority class. These methods further deteriorate on small, imbalanced data that have a large degree of class overlap. In this paper, we propose a novel discriminative sparse neighbor ... View full abstract»

• ### On Deep Learning for Trust-Aware Recommendations in Social Networks

Publication Year: 2017, Page(s):1164 - 1177
Cited by:  Papers (2)
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With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommend... View full abstract»

• ### Ristretto: A Framework for Empirical Study of Resource-Efficient Inference in Convolutional Neural Networks

Publication Year: 2018, Page(s):1 - 6
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Convolutional neural networks (CNNs) have led to remarkable progress in a number of key pattern recognition tasks, such as visual scene understanding and speech recognition, that potentially enable numerous applications. Consequently, there is a significant need to deploy trained CNNs to resource-constrained embedded systems. Inference using pretrained modern deep CNNs, however, requires significa... View full abstract»

• ### Feature Selection Based on Structured Sparsity: A Comprehensive Study

Publication Year: 2017, Page(s):1490 - 1507
Cited by:  Papers (13)
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Feature selection (FS) is an important component of many pattern recognition tasks. In these tasks, one is often confronted with very high-dimensional data. FS algorithms are designed to identify the relevant feature subset from the original features, which can facilitate subsequent analysis, such as clustering and classification. Structured sparsity-inducing feature selection (SSFS) methods have ... View full abstract»

• ### $L_{1/2}$ Regularization: A Thresholding Representation Theory and a Fast Solver

Publication Year: 2012, Page(s):1013 - 1027
Cited by:  Papers (181)
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The special importance of L1/2 regularization has been recognized in recent studies on sparse modeling (particularly on compressed sensing). The L1/2 regularization, however, leads to a nonconvex, nonsmooth, and non-Lipschitz optimization problem that is difficult to solve fast and efficiently. In this paper, through developing a threshoding representation theory for L1/... View full abstract»

• ### Blind Image Quality Assessment via Deep Learning

Publication Year: 2015, Page(s):1275 - 1286
Cited by:  Papers (44)
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This paper investigates how to blindly evaluate the visual quality of an image by learning rules from linguistic descriptions. Extensive psychological evidence shows that humans prefer to conduct evaluations qualitatively rather than numerically. The qualitative evaluations are then converted into the numerical scores to fairly benchmark objective image quality assessment (IQA) metrics. Recently, ... View full abstract»

• ### Robust Estimation for Neural Networks With Randomly Occurring Distributed Delays and Markovian Jump Coupling

Publication Year: 2018, Page(s):845 - 855
Cited by:  Papers (4)
| | PDF (1103 KB) | HTML

This paper studies the issue of robust state estimation for coupled neural networks with parameter uncertainty and randomly occurring distributed delays, where the polytopic model is employed to describe the parameter uncertainty. A set of Bernoulli processes with different stochastic properties are introduced to model the randomly occurrences of the distributed delays. Novel state estimators base... View full abstract»

• ### Discriminative Deep Quantization Hashing for Face Image Retrieval

Publication Year: 2018, Page(s):1 - 9
| | PDF (1956 KB)

This paper proposes a new discriminative deep quantization hashing (DDQH) approach for large-scale face image retrieval by learning discriminative and compact binary codes. It jointly explores the discrete code learning, batch normalization quantization (BNQ) module, and end-to-end learning in one unified framework, which can guarantee the optimal compatibility of hash coding and feature learning.... View full abstract»

• ### Neural Network-Based Model-Free Adaptive Near-Optimal Tracking Control for a Class of Nonlinear Systems

Publication Year: 2018, Page(s):1 - 15
| | PDF (5059 KB)

In this paper, the receding horizon near-optimal tracking control problem about a class of continuous-time nonlinear systems with fully unknown dynamics is considered. The main challenges of this problem lie in two aspects: 1) most existing systems only restrict their considerations to the state feedback part while the input channel parameters are assumed to be known. This paper considers fully un... View full abstract»

• ### Model-Based Adaptive Event-Triggered Control of Strict-Feedback Nonlinear Systems

Publication Year: 2018, Page(s):1033 - 1045
Cited by:  Papers (3)
| | PDF (1758 KB) | HTML

This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN... View full abstract»

• ### Solving Multiextremal Problems by Using Recurrent Neural Networks

Publication Year: 2018, Page(s):1562 - 1574
Cited by:  Papers (1)
| | PDF (2520 KB) | HTML

In this paper, a neural network model for solving a class of multiextremal smooth nonconvex constrained optimization problems is proposed. Neural network is designed in such a way that its equilibrium points coincide with the local and global optimal solutions of the corresponding optimization problem. Based on the suitable underestimators for the Lagrangian of the problem, one give geometric crit... View full abstract»

• ### Unified Simultaneous Clustering and Feature Selection for Unlabeled and Labeled Data

Publication Year: 2018, Page(s):1 - 16
| | PDF (5479 KB)

This paper proposes a novel feature selection method, namely, unified simultaneous clustering feature selection (USCFS). A regularized regression with a new type of target matrix is formulated to select the most discriminative features among the original features from labeled or unlabeled data. The regression with l2,1-norm regularization allows the projection matrix to represent an effective sele... View full abstract»

• ### A Novel Pruning Algorithm for Smoothing Feedforward Neural Networks Based on Group Lasso Method

Publication Year: 2018, Page(s):2012 - 2024
| | PDF (3900 KB) | HTML

In this paper, we propose four new variants of the backpropagation algorithm to improve the generalization ability for feedforward neural networks. The basic idea of these methods stems from the Group Lasso concept which deals with the variable selection problem at the group level. There are two main drawbacks when the Group Lasso penalty has been directly employed during network training. They ar... View full abstract»

• ### Runtime Programmable and Memory Bandwidth Optimized FPGA-Based Coprocessor for Deep Convolutional Neural Network

Publication Year: 2018, Page(s):1 - 13
| | PDF (3420 KB)

The deep convolutional neural network (DCNN) is a class of machine learning algorithms based on feed-forward artificial neural network and is widely used for image processing applications. Implementation of DCNN in real-world problems needs high computational power and high memory bandwidth, in a power-constrained environment. A general purpose CPU cannot exploit different parallelisms offered by ... View full abstract»

## 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