# IEEE Transactions on Cybernetics

## Volume 47 Issue 10 • Oct. 2017

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## Filter Results

Displaying Results 1 - 25 of 46

Publication Year: 2017, Page(s):C1 - 2969
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• ### IEEE Transactions on Cybernetics

Publication Year: 2017, Page(s): C2
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• ### Convergence and Multistability of Nonsymmetric Cellular Neural Networks With Memristors

Publication Year: 2017, Page(s):2970 - 2983
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Recent work has considered a class of cellular neural networks (CNNs) where each cell contains an ideal capacitor and an ideal flux-controlled memristor. One main feature is that during the analog computation the memristor is assumed to be a dynamic element, hence each cell is second-order with state variables given by the capacitor voltage and the memristor flux. Such CNNs, named dynamic memristo... View full abstract»

• ### Scale-Limited Lagrange Stability and Finite-Time Synchronization for Memristive Recurrent Neural Networks on Time Scales

Publication Year: 2017, Page(s):2984 - 2994
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The existed results of Lagrange stability and finite-time synchronization for memristive recurrent neural networks (MRNNs) are scale-free on time evolvement, and some restrictions appear naturally. In this paper, two novel scale-limited comparison principles are established by means of inequality techniques and induction principle on time scales. Then the results concerning Lagrange stability and ... View full abstract»

• ### Finite-Time Synchronization of Coupled Hierarchical Hybrid Neural Networks With Time-Varying Delays

Publication Year: 2017, Page(s):2995 - 3004
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This paper is concerned with the finite-time synchronization problem of coupled hierarchical hybrid delayed neural networks. This coupled hierarchical hybrid neural networks consist of a higher level switching and a lower level Markovian jumping. The time-varying delays are dependent on not only switching signal but also jumping mode. By using a less conservative weighted integral inequality and s... View full abstract»

• ### Synchronization of Reaction–Diffusion Neural Networks With Dirichlet Boundary Conditions and Infinite Delays

Publication Year: 2017, Page(s):3005 - 3017
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This paper is concerned with synchronization for a class of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite discrete time-varying delays. By utilizing theories of partial differential equations, Green's formula, inequality techniques, and the concept of comparison, algebraic criteria are presented to guarantee master-slave synchronization of the underlying reacti... View full abstract»

• ### Adaptive Neural Synchronization Control for Bilateral Teleoperation Systems With Time Delay and Backlash-Like Hysteresis

Publication Year: 2017, Page(s):3018 - 3026
Cited by:  Papers (3)
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This paper considers the master and slave synchronization control for bilateral teleoperation systems with time delay and backlash-like hysteresis. Based on radial basis functions neural networks' approximation capabilities, two improved adaptive neural control approaches are developed. By Lyapunov stability analysis, the position and velocity tracking errors are guaranteed to converge to a small ... View full abstract»

• ### Exponential Stabilization of Memristive Neural Networks via Saturating Sampled-Data Control

Publication Year: 2017, Page(s):3027 - 3039
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This paper is concerned with the exponential stabilization of memristive neural networks (MNNs) by taking into account the sampled-data control and actuator saturation. On the one hand, the MNNs are converted into a tractable model by defining a class of logical switched functions. Based on this model, the connection weights of MNNs are dealt with by a robust analysis method. On the other hand, a ... View full abstract»

• ### Stability Analysis of Discrete-Time Neural Networks With Time-Varying Delay via an Extended Reciprocally Convex Matrix Inequality

Publication Year: 2017, Page(s):3040 - 3049
Cited by:  Papers (1)
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This paper is concerned with the stability analysis of discrete-time neural networks with a time-varying delay. Assessment of the effect of time delays on system stability requires suitable delay-dependent stability criteria. This paper aims to develop new stability criteria for reduction of conservatism without much increase of computational burden. An extended reciprocally convex matrix inequali... View full abstract»

• ### A Neurodynamic Model to Solve Nonlinear Pseudo-Monotone Projection Equation and Its Applications

Publication Year: 2017, Page(s):3050 - 3062
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In this paper, a neurodynamic model is given to solve nonlinear pseudo-monotone projection equation. Under pseudo-monotonicity condition and Lipschitz continuous condition, the projection neurodynamic model is proved to be stable in the sense of Lyapunov, globally convergent, globally asymptotically stable, and globally exponentially stable. Also, we show that, our new neurodynamic model is effect... View full abstract»

• ### A One-Layer Recurrent Neural Network for Pseudoconvex Optimization Problems With Equality and Inequality Constraints

Publication Year: 2017, Page(s):3063 - 3074
Cited by:  Papers (1)
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Pseudoconvex optimization problem, as an important nonconvex optimization problem, plays an important role in scientific and engineering applications. In this paper, a recurrent one-layer neural network is proposed for solving the pseudoconvex optimization problem with equality and inequality constraints. It is proved that from any initial state, the state of the proposed neural network reaches th... View full abstract»

• ### Adaptive Neural Tracking Control for a Class of Nonlinear Systems With Dynamic Uncertainties

Publication Year: 2017, Page(s):3075 - 3087
Cited by:  Papers (11)
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This paper considers the problem of adaptive neural control of nonlower triangular nonlinear systems with unmodeled dynamics and dynamic disturbances. The design difficulties appeared in the unmodeled dynamics and nonlower triangular form are handled with a dynamic signal and a variable partition technique for the nonlinear functions of all state variables, respectively. It is shown that the propo... View full abstract»

• ### Adaptive Neural Tracking Control for Switched High-Order Stochastic Nonlinear Systems

Publication Year: 2017, Page(s):3088 - 3099
Cited by:  Papers (2)
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This paper deals with adaptive neural tracking control design for a class of switched high-order stochastic nonlinear systems with unknown uncertainties and arbitrary deterministic switching. The considered issues are: 1) completely unknown uncertainties; 2) stochastic disturbances; and 3) high-order nonstrict-feedback system structure. The considered mathematical models can represent many practic... View full abstract»

• ### Approximation-Based Adaptive Neural Tracking Control of Nonlinear MIMO Unknown Time-Varying Delay Systems With Full State Constraints

Publication Year: 2017, Page(s):3100 - 3109
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This paper deals with the tracking control problem for a class of nonlinear multiple input multiple output unknown time-varying delay systems with full state constraints. To overcome the challenges which cause by the appearances of the unknown time-varying delays and full-state constraints simultaneously in the systems, an adaptive control method is presented for such systems for the first time. T... View full abstract»

• ### Adaptive Neural Control of Uncertain Nonlinear Systems Using Disturbance Observer

Publication Year: 2017, Page(s):3110 - 3123
Cited by:  Papers (1)
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This paper studies the problem of prescribed performance adaptive neural control for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems in the presence of external disturbances and input saturation based on a disturbance observer. The system uncertainties are tackled by neural network (NN) approximation. To handle unknown disturbances, a Nussbaum disturbance observer is pre... View full abstract»

• ### On Stabilization of Quantized Sampled-Data Neural-Network-Based Control Systems

Publication Year: 2017, Page(s):3124 - 3135
Cited by:  Papers (2)
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This paper investigates the problem of stabilization of sampled-data neural-network-based systems with state quantization. Different with previous works, the communication limitation of state quantization is considered for the first time. More specifically, it is assumed that the sampled state measurements from sensor to the controller are quantized via a quantizer. To reduce conservativeness, a n... View full abstract»

• ### Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints

Publication Year: 2017, Page(s):3136 - 3147
Cited by:  Papers (1)
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The control problem of an uncertain n -degrees of freedom robotic manipulator subjected to time-varying output constraints is investigated in this paper. We describe the rigid robotic manipulator system as a multi-input and multi-output nonlinear system. We devise a disturbance observer to estimate the unknown disturbance from humans and environment. To solve the uncertain problem, a neural networ... View full abstract»

• ### Neural-Learning-Based Telerobot Control With Guaranteed Performance

Publication Year: 2017, Page(s):3148 - 3159
Cited by:  Papers (9)
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In this paper, a neural networks (NNs) enhanced telerobot control system is designed and tested on a Baxter robot. Guaranteed performance of the telerobot control system is achieved at both kinematic and dynamic levels. At kinematic level, automatic collision avoidance is achieved by the control design at the kinematic level exploiting the joint space redundancy, thus the human operator would be a... View full abstract»

• ### Command Filter-Based Adaptive Neural Tracking Controller Design for Uncertain Switched Nonlinear Output-Constrained Systems

Publication Year: 2017, Page(s):3160 - 3171
Cited by:  Papers (7)
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In this paper, a new adaptive approximation-based tracking controller design approach is developed for a class of uncertain nonlinear switched lower-triangular systems with an output constraint using neural networks (NNs). By introducing a novel barrier Lyapunov function (BLF), the constrained switched system is first transformed into a new system without any constraint, which means the control ob... View full abstract»

• ### Trajectory Predictor by Using Recurrent Neural Networks in Visual Tracking

Publication Year: 2017, Page(s):3172 - 3183
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Motion models have been proved to be a crucial part in the visual tracking process. In recent trackers, particle filter and sliding windows-based motion models have been widely used. Treating motion models as a sequence prediction problem, we can estimate the motion of objects using their trajectories. Moreover, it is possible to transfer the learned knowledge from annotated trajectories to new ob... View full abstract»

• ### Neuronal State Estimation for Neural Networks With Two Additive Time-Varying Delay Components

Publication Year: 2017, Page(s):3184 - 3194
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This paper is concerned with the state estimation for neural networks with two additive time-varying delay components. Three cases of these two time-varying delays are fully considered: 1) both delays are differentiable uniformly bounded with delay-derivative bounded by some constants; 2) one delay is continuous uniformly bounded while the other is differentiable uniformly bounded with delay-deriv... View full abstract»

• ### Filtering of Discrete-Time Switched Neural Networks Ensuring Exponential Dissipative and $l_{2}$ – $l_{infty }$ Performances

Publication Year: 2017, Page(s):3195 - 3207
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This paper studies delay-dependent exponential dissipative and l2-l∞ filtering problems for discrete-time switched neural networks (DSNNs) including time-delayed states. By introducing a novel discrete-time inequality, which is a discrete-time version of the continuous-time Wirtinger-type inequality, we establish new sets of linear matrix inequality (LMI) criteria such... View full abstract»

• ### Reachable Set Estimation for Markovian Jump Neural Networks With Time-Varying Delays

Publication Year: 2017, Page(s):3208 - 3217
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In this paper, the reachable set estimation problem is investigated for Markovian jump neural networks (NNs) with time-varying delays and bounded peak disturbances. Our goal is to find a set as small as possible which bounds all the state trajectories of the NNs under zero initial conditions. In the framework of Lyapunov-Krasovskii theorem, a newly-found summation inequality combined with the reci... View full abstract»

• ### Building Correlations Between Filters in Convolutional Neural Networks

Publication Year: 2017, Page(s):3218 - 3229
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In this paper, a new optimization approach is designed for convolutional neural network (CNN) which introduces explicit logical relations between filters in the convolutional layer. In a conventional CNN, the filters' weights in convolutional layers are separately trained by their own residual errors, and the relations of these filters are not explored for learning. Different from the traditional ... View full abstract»

• ### Multiview Convolutional Neural Networks for Multidocument Extractive Summarization

Publication Year: 2017, Page(s):3230 - 3242
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Multidocument summarization has gained popularity in many real world applications because vital information can be extracted within a short time. Extractive summarization aims to generate a summary of a document or a set of documents by ranking sentences and the ranking results rely heavily on the quality of sentence features. However, almost all previous algorithms require hand-crafted features f... View full abstract»

## Aims & Scope

The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Prof. Jun Wang
Dept. of Computer Science
City University of Hong Kong
Kowloon Tong, Kowloon, Hong Kong
Tel: +852 34429701
Email: jwang.cs@cityu.edu.hk