# IEEE Transactions on Cybernetics

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• ### Complex and Concurrent Negotiations for Multiple Interrelated e-Markets

Publication Year: 2013, Page(s):230 - 245
Cited by:  Papers (21)
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To date, most of the existing bargaining models are designed for supporting negotiation in only one market involving only two types of participants (buyers and sellers). This work devises a complex negotiation mechanism that supports negotiation activities among three types of participants in multiple interrelated markets. The complex negotiation mechanism consists of: 1) a bargaining-position-est... View full abstract»

• ### Enhanced Computer Vision With Microsoft Kinect Sensor: A Review

Publication Year: 2013, Page(s):1318 - 1334
Cited by:  Papers (329)  |  Patents (25)
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With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision al... View full abstract»

• ### Recent Development in Big Data Analytics for Business Operations and Risk Management

Publication Year: 2017, Page(s):81 - 92
Cited by:  Papers (13)
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“Big data” is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., w... View full abstract»

• ### An Overview of Recent Advances in Event-Triggered Consensus of Multiagent Systems

Publication Year: 2018, Page(s):1110 - 1123
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Event-triggered consensus of multiagent systems (MASs) has attracted tremendous attention from both theoretical and practical perspectives due to the fact that it enables all agents eventually to reach an agreement upon a common quantity of interest while significantly alleviating utilization of communication and computation resources. This paper aims to provide an overview of recent advances in e... View full abstract»

• ### Genetic Learning Particle Swarm Optimization

Publication Year: 2016, Page(s):2277 - 2290
Cited by:  Papers (16)
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Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is ... View full abstract»

• ### A Generic Deep-Learning-Based Approach for Automated Surface Inspection

Publication Year: 2018, Page(s):929 - 940
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Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning ne... View full abstract»

• ### Automatic Facial Expression Recognition System Using Deep Network-Based Data Fusion

Publication Year: 2018, Page(s):103 - 114
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This paper presents a novel automatic facial expressions recognition system (AFERS) using the deep network framework. The proposed AFERS consists of four steps: 1) geometric features extraction; 2) regional local binary pattern (LBP) features extraction; 3) fusion of both the features using autoencoders; and 4) classification using Kohonen self-organizing map (SOM)-based classifier. This paper mak... 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»

• ### Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach

Publication Year: 2013, Page(s):1656 - 1671
Cited by:  Papers (130)
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Classification problems often have a large number of features in the data sets, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the performance. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main conflicting objectives of maxim... View full abstract»

• ### Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection

Publication Year: 2014, Page(s):793 - 804
Cited by:  Papers (82)
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Feature selection has aroused considerable research interests during the last few decades. Traditional learning-based feature selection methods separate embedding learning and feature ranking. In this paper, we propose a novel unsupervised feature selection framework, termed as the joint embedding learning and sparse regression (JELSR), in which the embedding learning and sparse regression are joi... View full abstract»

• ### Simulating Kinect Infrared and Depth Images

Publication Year: 2016, Page(s):3018 - 3031
Cited by:  Papers (2)
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With the emergence of the Microsoft Kinect sensor, many developer communities and research groups have found countless uses and have already published a wide variety of papers that utilize the raw depth images for their specific goals. New methods and applications that use the device generally require an appropriately large ensemble of data sets with accompanying ground truth for testing purposes,... View full abstract»

• ### Stacked Convolutional Denoising Auto-Encoders for Feature Representation

Publication Year: 2017, Page(s):1017 - 1027
Cited by:  Papers (13)
| | PDF (1793 KB) | HTML

Deep networks have achieved excellent performance in learning representation from visual data. However, the supervised deep models like convolutional neural network require large quantities of labeled data, which are very expensive to obtain. To solve this problem, this paper proposes an unsupervised deep network, called the stacked convolutional denoising auto-encoders, which can map images to hi... View full abstract»

• ### Coupled Deep Autoencoder for Single Image Super-Resolution

Publication Year: 2017, Page(s):27 - 37
Cited by:  Papers (12)
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Sparse coding has been widely applied to learning-based single image super-resolution (SR) and has obtained promising performance by jointly learning effective representations for low-resolution (LR) and high-resolution (HR) image patch pairs. However, the resulting HR images often suffer from ringing, jaggy, and blurring artifacts due to the strong yet ad hoc assumptions that the LR image patch r... View full abstract»

• ### Joint Feature Selection and Classification for Multilabel Learning

Publication Year: 2018, Page(s):876 - 889
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Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel cl... View full abstract»

• ### An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine

Publication Year: 2017, Page(s):920 - 933
Cited by:  Papers (8)
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This paper proposes a computationally efficient method for traffic sign recognition (TSR). This proposed method consists of two modules: (1) extraction of histogram of oriented gradient variant (HOGv) feature and (2) a single classifier trained by extreme learning machine (ELM) algorithm. The presented HOGv feature keeps a good balance between redundancy and local details such that it can represen... View full abstract»

• ### Distributed Online One-Class Support Vector Machine for Anomaly Detection Over Networks

Publication Year: 2018, Page(s):1 - 14
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Anomaly detection has attracted much attention in recent years since it plays a crucial role in many domains. Various anomaly detection approaches have been proposed, among which one-class support vector machine (OCSVM) is a popular one. In practice, data used for anomaly detection can be distributively collected via wireless sensor networks. Besides, as the data usually arrive at the nodes sequen... View full abstract»

• ### Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints

Publication Year: 2016, Page(s):620 - 629
Cited by:  Papers (165)
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This paper studies the tracking control problem for an uncertain ${n}$ -link robot with full-state constraints. The rigid robotic manipulator is described as a multiinput and multioutput system. Adaptive neural network (NN) control for the robotic system with full-state constraints is designed. In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances. The... View full abstract»

• ### Cross-Modal Retrieval With CNN Visual Features: A New Baseline

Publication Year: 2017, Page(s):449 - 460
Cited by:  Papers (5)
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Recently, convolutional neural network (CNN) visual features have demonstrated their powerful ability as a universal representation for various recognition tasks. In this paper, cross-modal retrieval with CNN visual features is implemented with several classic methods. Specifically, off-the-shelf CNN visual features are extracted from the CNN model, which is pretrained on ImageNet with more than o... View full abstract»

• ### An Adaptive Framework to Tune the Coordinate Systems in Nature-Inspired Optimization Algorithms

Publication Year: 2018, Page(s):1 - 14
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The performance of many nature-inspired optimization algorithms (NIOAs) depends strongly on their implemented coordinate system. However, the commonly used coordinate system is fixed and not well suited for different function landscapes, NIOAs thus might not search efficiently. To overcome this shortcoming, in this paper we propose a framework, named ACoS, to adaptively tune the coordinate systems... View full abstract»

• ### Consensus of Linear Multi-Agent Systems by Distributed Event-Triggered Strategy

Publication Year: 2016, Page(s):148 - 157
Cited by:  Papers (75)
| | PDF (1008 KB) | HTML

This paper studies the consensus problem of multi-agent systems with general linear dynamics. We propose a novel event-triggered control scheme with some desirable features, namely, distributed, asynchronous, and independent. It is shown that consensus of the controlled multi-agent system can be reached asymptotically. The feasibility of the event-triggered strategy is further verified by the excl... View full abstract»

• ### Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems

Publication Year: 2017, Page(s):1743 - 1756
Cited by:  Papers (3)
| | PDF (1832 KB) | HTML Media

For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this ... View full abstract»

• ### A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition

Publication Year: 2017, Page(s):1496 - 1509
Cited by:  Papers (7)
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This paper proposes a facial expression recognition system using evolutionary particle swarm optimization (PSO)-based feature optimization. The system first employs modified local binary patterns, which conduct horizontal and vertical neighborhood pixel comparison, to generate a discriminative initial facial representation. Then, a PSO variant embedded with the concept of a micro genetic algorithm... View full abstract»

• ### An Adaptive Multiobjective Particle Swarm Optimization Based on Multiple Adaptive Methods

Publication Year: 2017, Page(s):2754 - 2767
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Multiobjective particle swarm optimization (MOPSO) algorithms have attracted much attention for their promising performance in solving multiobjective optimization problems (MOPs). In this paper, an adaptive MOPSO (AMOPSO) algorithm, based on a hybrid framework of the solution distribution entropy and population spacing (SP) information, is developed to improve the search performance in terms of co... View full abstract»

• ### Consensus of Heterogeneous Linear Multiagent Systems Subject to Aperiodic Sampled-Data and DoS Attack

Publication Year: 2018, Page(s):1 - 11
| | PDF (705 KB)

In this paper, the robust output consensus problem for a class of heterogeneous linear multiagent systems (MASs) in presence of aperiodic sampling and random deny-of-service (DoS) attack is investigated. A novel distributed output-feedback control strategy is proposed so that the controlled MAS achieves the objective of output consensus in spite of aperiodic sampling and DoS attack. By assuming th... View full abstract»

• ### Time-Delay Neural Network for Continuous Emotional Dimension Prediction From Facial Expression Sequences

Publication Year: 2016, Page(s):916 - 929
Cited by:  Papers (9)
| | PDF (1551 KB) | HTML

Automatic continuous affective state prediction from naturalistic facial expression is a very challenging research topic but very important in human-computer interaction. One of the main challenges is modeling the dynamics that characterize naturalistic expressions. In this paper, a novel two-stage automatic system is proposed to continuously predict affective dimension values from facial expressi... View full abstract»

• ### A Q-Learning Approach to Flocking With UAVs in a Stochastic Environment

Publication Year: 2017, Page(s):186 - 197
Cited by:  Papers (3)
| | PDF (1542 KB) | HTML

In the past two decades, unmanned aerial vehicles (UAVs) have demonstrated their efficacy in supporting both military and civilian applications, where tasks can be dull, dirty, dangerous, or simply too costly with conventional methods. Many of the applications contain tasks that can be executed in parallel, hence the natural progression is to deploy multiple UAVs working together as a force multip... View full abstract»

• ### Adaptive Critic Nonlinear Robust Control: A Survey

Publication Year: 2017, Page(s):3429 - 3451
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Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and s... View full abstract»

• ### A Novel Deep Learning-Based Collaborative Filtering Model for Recommendation System

Publication Year: 2018, Page(s):1 - 13
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The collaborative filtering (CF) based models are capable of grasping the interaction or correlation of users and items under consideration. However, existing CF-based methods can only grasp single type of relation, such as restricted Boltzmann machine which distinctly seize the correlation of user-user or item-item relation. On the other hand, matrix factorization explicitly captures the interact... View full abstract»

• ### A Feature Selection and Classification Algorithm Based on Randomized Extraction of Model Populations

Publication Year: 2018, Page(s):1151 - 1162
| | PDF (1069 KB) | HTML

We here introduce a novel classification approach adopted from the nonlinear model identification framework, which jointly addresses the feature selection (FS) and classifier design tasks. The classifier is constructed as a polynomial expansion of the original features and a selection process is applied to find the relevant model terms. The selection method progressively refines a probability dist... View full abstract»

• ### Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking

Publication Year: 2017, Page(s):4014 - 4024
Cited by:  Papers (4)
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How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. T... View full abstract»

• ### Adaptive Robust Tracking Control for Multiple Unknown Fractional-Order Nonlinear Systems

Publication Year: 2018, Page(s):1 - 12
| | PDF (1921 KB)

By applying the fractional Lyapunov direct method, we investigate the robust consensus tracking problem for a class of uncertain fractional-order multiagent systems with a leader whose input is unknown and bounded. More specifically, multiple fractional-order systems with heterogeneous unknown nonlinearities and external disturbances are considered in this paper, which include the second-order mul... View full abstract»

• ### Constrained Superpixel Tracking

Publication Year: 2018, Page(s):1030 - 1041
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In this paper, we propose a constrained graph labeling algorithm for visual tracking where nodes denote superpixels and edges encode the underlying spatial, temporal, and appearance fitness constraints. First, the spatial smoothness constraint, based on a transductive learning method, is enforced to leverage the latent manifold structure in feature space by investigating unlabeled superpixels in t... View full abstract»

• ### A Self-Adaptive Sleep/Wake-Up Scheduling Approach for Wireless Sensor Networks

Publication Year: 2018, Page(s):979 - 992
| | PDF (1063 KB) | HTML Media

Sleep/wake-up scheduling is one of the fundamental problems in wireless sensor networks, since the energy of sensor nodes is limited and they are usually unrechargeable. The purpose of sleep/wake-up scheduling is to save the energy of each node by keeping nodes in sleep mode as long as possible (without sacrificing packet delivery efficiency) and thereby maximizing their lifetime. In this paper, a... View full abstract»

• ### Spatial-Temporal Recurrent Neural Network for Emotion Recognition

Publication Year: 2018, Page(s):1 - 9
| | PDF (1706 KB)

In this paper, we propose a novel deep learning framework, called spatial-temporal recurrent neural network (STRNN), to integrate the feature learning from both spatial and temporal information of signal sources into a unified spatial-temporal dependency model. In STRNN, to capture those spatially co-occurrent variations of human emotions, a multidirectional recurrent neural network (RNN) layer is... View full abstract»

• ### Denoising Hyperspectral Image With Non-i.i.d. Noise Structure

Publication Year: 2018, Page(s):1054 - 1066
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Hyperspectral image (HSI) denoising has been attracting much research attention in remote sensing area due to its importance in improving the HSI qualities. The existing HSI denoising methods mainly focus on specific spectral and spatial prior knowledge in HSIs, and share a common underlying assumption that the embedded noise in HSI is independent and identically distributed (i.i.d.). In real scen... View full abstract»

• ### Robust Gait Recognition by Integrating Inertial and RGBD Sensors

Publication Year: 2018, Page(s):1136 - 1150
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Gait has been considered as a promising and unique biometric for person identification. Traditionally, gait data are collected using either color sensors, such as a CCD camera, depth sensors, such as a Microsoft Kinect, or inertial sensors, such as an accelerometer. However, a single type of sensors may only capture part of the dynamic gait features and make the gait recognition sensitive to compl... View full abstract»

• ### Distributed Event-Triggered Cooperative Control for Frequency and Voltage Stability and Power Sharing in Isolated Inverter-Based Microgrid

Publication Year: 2018, Page(s):1 - 13
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The distributed cooperative control for frequency and voltage stability and power sharing in microgrid considering the limitation of communication network is concerned in this paper. Two types of novel event-triggered mechanism with distributed architecture are first proposed, which can greatly reduce the communication burdens among power source inverters. Based on the event-triggered schemes, dis... View full abstract»

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

Publication Year: 2017, Page(s):3148 - 3159
Cited by:  Papers (9)
| | PDF (3029 KB) | HTML

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»

• ### Body Joint Guided 3-D Deep Convolutional Descriptors for Action Recognition

Publication Year: 2018, Page(s):1095 - 1108
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3-D convolutional neural networks (3-D CNNs) have been established as a powerful tool to simultaneously learn features from both spatial and temporal dimensions, which is suitable to be applied to video-based action recognition. In this paper, we propose not to directly use the activations of fully connected layers of a 3-D CNN as the video feature, but to use selective convolutional layer activat... View full abstract»

• ### Adaptive Neural Tracking Control for Interconnected Switched Systems With Non-ISS Unmodeled Dynamics

Publication Year: 2018, Page(s):1 - 11
| | PDF (1538 KB)

The adaptive neural network tracking control problem is investigated for a class of interconnected switched systems. The considered systems are with unmodeled dynamics, some of which do not satisfy the input-to-state stable (ISS) condition. By utilizing the neural network to approximate the composite unknown nonlinear functions, the corresponding decentralized tracking controller is designed for e... View full abstract»

• ### Exact and Approximate Stability of Solutions to Traveling Salesman Problems

Publication Year: 2018, Page(s):583 - 595
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This paper presents the stability analysis of an optimal tour for the symmetric traveling salesman problem (TSP) by obtaining stability regions. The stability region of an optimal tour is the set of all cost changes for which that solution remains optimal and can be understood as the margin of optimality for a solution with respect to perturbations in the problem data. It is known that it is not p... View full abstract»

• ### Robust Graph-Based Semisupervised Learning for Noisy Labeled Data via Maximum Correntropy Criterion

Publication Year: 2018, Page(s):1 - 14
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Semisupervised learning (SSL) methods have been proved to be effective at solving the labeled samples shortage problem by using a large number of unlabeled samples together with a small number of labeled samples. However, many traditional SSL methods may not be robust with too much labeling noisy data. To address this issue, in this paper, we propose a robust graph-based SSL method based on maximu... View full abstract»

• ### Weighted Joint Sparse Representation for Removing Mixed Noise in Image

Publication Year: 2017, Page(s):600 - 611
Cited by:  Papers (9)
| | PDF (2983 KB) | HTML

Joint sparse representation (JSR) has shown great potential in various image processing and computer vision tasks. Nevertheless, the conventional JSR is fragile to outliers. In this paper, we propose a weighted JSR (WJSR) model to simultaneously encode a set of data samples that are drawn from the same subspace but corrupted with noise and outliers. Our model is desirable to exploit the common inf... View full abstract»

• ### Finite Time Fault Tolerant Control for Robot Manipulators Using Time Delay Estimation and Continuous Nonsingular Fast Terminal Sliding Mode Control

Publication Year: 2017, Page(s):1681 - 1693
Cited by:  Papers (7)
| | PDF (1480 KB) | HTML

In this paper, a novel finite time fault tolerant control (FTC) is proposed for uncertain robot manipulators with actuator faults. First, a finite time passive FTC (PFTC) based on a robust nonsingular fast terminal sliding mode control (NFTSMC) is investigated. Be analyzed for addressing the disadvantages of the PFTC, an AFTC are then investigated by combining NFTSMC with a simple fault diagnosis ... View full abstract»

• ### Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons

Publication Year: 2017, Page(s):198 - 211
Cited by:  Papers (5)
| | PDF (2178 KB) | HTML Media

Dynamic multiobjective optimization (DMO) has received growing research interest in recent years since many real-world optimization problems appear to not only have multiple objectives that conflict with each other but also change over time. The time-varying characteristics of these DMO problems (DMOPs) pose new challenges to evolutionary algorithms. Considering the importance of a representative ... View full abstract»

• ### A Bio-Inspired Approach to Traffic Network Equilibrium Assignment Problem

Publication Year: 2018, Page(s):1304 - 1315
| | PDF (2180 KB) | HTML

Finding an equilibrium state of the traffic assignment plays a significant role in the design of transportation networks. We adapt the path finding mathematical model of slime mold Physarum polycephalum to solve the traffic equilibrium assignment problem. We make three contributions in this paper. First, we propose a generalized Physarum model to solve the shortest path problem in directed and asy... View full abstract»

• ### Tracking Control of a Class of Cyber-Physical Systems via a FlexRay Communication Network

Publication Year: 2018, Page(s):1 - 14
| | PDF (1301 KB)

Due to properties of flexibility, adaptiveness, error tolerance, and time-determinism performance, the FlexRay communication protocol has been widely used to investigate robot systems and new generation of automobiles. In this paper, with the FlexRay communication protocol, the tracking problem of a class of cyber-physical systems are investigated by developing a general hybrid model, in which an ... View full abstract»

• ### Event-Triggered Schemes on Leader-Following Consensus of General Linear Multiagent Systems Under Different Topologies

Publication Year: 2017, Page(s):212 - 223
Cited by:  Papers (20)
| | PDF (1376 KB) | HTML

This paper investigates the leader-following consensus for multiagent systems with general linear dynamics by means of event-triggered scheme (ETS). We propose three types of schemes, namely, distributed ETS (distributed-ETS), centralized ETS (centralized-ETS), and clustered ETS (clustered-ETS) for different network topologies. All these schemes guarantee that all followers can track the leader ev... View full abstract»

• ### Neural Network-Based Adaptive Leader-Following Consensus Control for a Class of Nonlinear Multiagent State-Delay Systems

Publication Year: 2017, Page(s):2151 - 2160
Cited by:  Papers (11)
| | PDF (1066 KB) | HTML

Compared with the existing neural network (NN) or fuzzy logic system (FLS) based adaptive consensus methods, the proposed approach can greatly alleviate the computation burden because it needs only to update a few adaptive parameters online. In the multiagent agreement control, the system uncertainties derive from the unknown nonlinear dynamics are counteracted by employing the adaptive NNs; the s... View full abstract»

• ### A Competitive Swarm Optimizer for Large Scale Optimization

Publication Year: 2015, Page(s):191 - 204
Cited by:  Papers (57)
| | PDF (3295 KB) | HTML

In this paper, a novel competitive swarm optimizer (CSO) for large scale optimization is proposed. The algorithm is fundamentally inspired by the particle swarm optimization but is conceptually very different. In the proposed CSO, neither the personal best position of each particle nor the global best position (or neighborhood best positions) is involved in updating the particles. Instead, a pairw... 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