# IEEE Transactions on Neural Networks and Learning Systems

## Filter Results

Displaying Results 1 - 25 of 30

Publication Year: 2016, Page(s):C1 - 209
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• ### IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Publication Year: 2016, Page(s): C2
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• ### Guest Editorial Special Issue on Neurodynamic Systems for Optimization and Applications

Publication Year: 2016, Page(s):210 - 213
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• ### A Bi-Projection Neural Network for Solving Constrained Quadratic Optimization Problems

Publication Year: 2016, Page(s):214 - 224
Cited by:  Papers (7)
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In this paper, a bi-projection neural network for solving a class of constrained quadratic optimization problems is proposed. It is proved that the proposed neural network is globally stable in the sense of Lyapunov, and the output trajectory of the proposed neural network will converge globally to an optimal solution. Compared with existing projection neural networks (PNNs), the proposed neural n... View full abstract»

• ### Taylor $O(h^{3})$ Discretization of ZNN Models for Dynamic Equality-Constrained Quadratic Programming With Application to Manipulators

Publication Year: 2016, Page(s):225 - 237
Cited by:  Papers (10)
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In this paper, a new Taylor-type numerical differentiation formula is first presented to discretize the continuous-time Zhang neural network (ZNN), and obtain higher computational accuracy. Based on the Taylor-type formula, two Taylor-type discrete-time ZNN models (termed Taylortype discrete-time ZNNK and Taylor-type discrete-time ZNNU models) are then proposed and discussed to perform online dyna... View full abstract»

• ### A Neurodynamic Approach for Real-Time Scheduling via Maximizing Piecewise Linear Utility

Publication Year: 2016, Page(s):238 - 248
Cited by:  Papers (1)
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In this paper, we study a set of real-time scheduling problems whose objectives can be expressed as piecewise linear utility functions. This model has very wide applications in scheduling-related problems, such as mixed criticality, response time minimization, and tardiness analysis. Approximation schemes and matrix vectorization techniques are applied to transform scheduling problems into linear ... View full abstract»

• ### Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms

Publication Year: 2016, Page(s):249 - 261
Cited by:  Papers (14)
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The optimization of real scalar functions of quaternion variables, such as the mean square error or array output power, underpins many practical applications. Solutions typically require the calculation of the gradient and Hessian. However, real functions of quaternion variables are essentially nonanalytic, which are prohibitive to the development of quaternion-valued learning systems. To address ... View full abstract»

• ### A New Continuous-Time Equality-Constrained Optimization to Avoid Singularity

Publication Year: 2016, Page(s):262 - 272
Cited by:  Papers (1)
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In equality-constrained optimization, a standard regularity assumption is often associated with feasible point methods, namely, that the gradients of constraints are linearly independent. In practice, the regularity assumption may be violated. In order to avoid such a singularity, a new projection matrix is proposed based on which a feasible point method to continuous-time, equality-constrained op... View full abstract»

• ### $L_{1}$ -Norm Low-Rank Matrix Decomposition by Neural Networks and Mollifiers

Publication Year: 2016, Page(s):273 - 283
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The L1 -norm cost function of the low-rank approximation of the matrix with missing entries is not smooth, and also cannot be transformed into a standard linear or quadratic programming problem, and thus, the optimization of this cost function is still not well solved. To tackle this problem, first, a mollifier is used to smooth the cost function. High closeness of the smoothed function... View full abstract»

• ### Zeroth-Order Method for Distributed Optimization With Approximate Projections

Publication Year: 2016, Page(s):284 - 294
Cited by:  Papers (5)
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This paper studies the problem of minimizing a sum of (possible nonsmooth) convex functions that are corresponding to multiple interacting nodes, subject to a convex state constraint set. Time-varying directed network is considered here. Two types of computational constraints are investigated in this paper: one where the information of gradients is not available and the other where the projection ... View full abstract»

• ### Nonsmooth Neural Network for Convex Time-Dependent Constraint Satisfaction Problems

Publication Year: 2016, Page(s):295 - 307
Cited by:  Papers (3)
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This paper introduces a nonsmooth (NS) neural network that is able to operate in a time-dependent (TD) context and is potentially useful for solving some classes of NS-TD problems. The proposed network is named nonsmooth time-dependent network (NTN) and is an extension to a TD setting of a previous NS neural network for programming problems. Suppose C(t), t ≥ 0, is a nonempty TD convex feas... View full abstract»

• ### A Generalized Hopfield Network for Nonsmooth Constrained Convex Optimization: Lie Derivative Approach

Publication Year: 2016, Page(s):308 - 321
Cited by:  Papers (32)
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This paper proposes a generalized Hopfield network for solving general constrained convex optimization problems. First, the existence and the uniqueness of solutions to the generalized Hopfield network in the Filippov sense are proved. Then, the Lie derivative is introduced to analyze the stability of the network using a differential inclusion. The optimality of the solution to the nonsmooth const... View full abstract»

• ### Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network

Publication Year: 2016, Page(s):322 - 333
Cited by:  Papers (15)
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The optimal formation problem of multirobot systems is solved by a recurrent neural network in this paper. The desired formation is described by the shape theory. This theory can generate a set of feasible formations that share the same relative relation among robots. An optimal formation means that finding one formation from the feasible formation set, which has the minimum distance to the initia... View full abstract»

• ### The Kernel Adaptive Autoregressive-Moving-Average Algorithm

Publication Year: 2016, Page(s):334 - 346
Cited by:  Papers (4)
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In this paper, we present a novel kernel adaptive recurrent filtering algorithm based on the autoregressive-moving-average (ARMA) model, which is trained with recurrent stochastic gradient descent in the reproducing kernel Hilbert spaces. This kernelized recurrent system, the kernel adaptive ARMA (KAARMA) algorithm, brings together the theories of adaptive signal processing and recurrent neural ne... View full abstract»

• ### Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network

Publication Year: 2016, Page(s):347 - 360
Cited by:  Papers (10)
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This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endanger... View full abstract»

• ### Bayesian Recurrent Neural Network for Language Modeling

Publication Year: 2016, Page(s):361 - 374
Cited by:  Papers (5)
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A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size ... View full abstract»

• ### Twin Neurons for Efficient Real-World Data Distribution in Networks of Neural Cliques: Applications in Power Management in Electronic Circuits

Publication Year: 2016, Page(s):375 - 387
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Associative memories are data structures that allow retrieval of previously stored messages given part of their content. They, thus, behave similarly to the human brain's memory that is capable, for instance, of retrieving the end of a song, given its beginning. Among different families of associative memories, sparse ones are known to provide the best efficiency (ratio of the number of bits store... View full abstract»

• ### Recurrent-Neural-Network-Based Multivariable Adaptive Control for a Class of Nonlinear Dynamic Systems With Time-Varying Delay

Publication Year: 2016, Page(s):388 - 401
Cited by:  Papers (2)
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At the beginning, an approximate nonlinear autoregressive moving average (NARMA) model is employed to represent a class of multivariable nonlinear dynamic systems with time-varying delay. It is known that the disadvantages of robust control for the NARMA model are as follows: 1) suitable control parameters for larger time delay are more sensitive to achieving desirable performance; 2) it only deal... View full abstract»

• ### Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network

Publication Year: 2016, Page(s):402 - 415
Cited by:  Papers (8)
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A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve t... 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»

• ### Optimal Communication Network-Based $H_infty$ Quantized Control With Packet Dropouts for a Class of Discrete-Time Neural Networks With Distributed Time Delay

Publication Year: 2016, Page(s):426 - 434
Cited by:  Papers (14)
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This paper is concerned with optimal communication network-based H∞ quantized control for a discrete-time neural network with distributed time delay. Control of the neural network (plant) is implemented via a communication network. Both quantization and communication network-induced data packet dropouts are considered simultaneously. It is assumed that the plant state signal is q... View full abstract»

• ### QoS Differential Scheduling in Cognitive-Radio-Based Smart Grid Networks: An Adaptive Dynamic Programming Approach

Publication Year: 2016, Page(s):435 - 443
Cited by:  Papers (11)
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As the next-generation power grid, smart grid will be integrated with a variety of novel communication technologies to support the explosive data traffic and the diverse requirements of quality of service (QoS). Cognitive radio (CR), which has the favorable ability to improve the spectrum utilization, provides an efficient and reliable solution for smart grid communications networks. In this paper... View full abstract»

• ### Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP

Publication Year: 2016, Page(s):444 - 458
Cited by:  Papers (41)
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This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input-output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal ... View full abstract»

• ### Synchronization and State Estimation of a Class of Hierarchical Hybrid Neural Networks With Time-Varying Delays

Publication Year: 2016, Page(s):459 - 470
Cited by:  Papers (18)
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This paper addresses the problems of synchronization and state estimation for a class of discrete-time hierarchical hybrid neural networks (NNs) with time-varying delays. The hierarchical hybrid feature consists of a higher level nondeterministic switching and a lower level stochastic switching. The latter is used to describe the NNs subject to Markovian modes transitions, whereas the former is of... View full abstract»

• ### A Switching Approach to Designing Finite-Time Synchronization Controllers of Coupled Neural Networks

Publication Year: 2016, Page(s):471 - 482
Cited by:  Papers (22)
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This paper is concerned with the finite-time synchronization issue of nonlinear coupled neural networks by designing a new switching pinning controller. For the fixed network topology and control strength, the newly designed controller could optimize the synchronization time by regulating a parameter α (0 ≤ α <; 1). The control law presented in this paper covers both contin... 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