By Topic

# IEEE Transactions on Cybernetics

### Early Access Articles

Early Access articles are made available in advance of the final electronic or print versions. Early Access articles are peer reviewed but may not be fully edited. They are fully citable from the moment they appear in IEEE Xplore.

## Filter Results

Displaying Results 1 - 25 of 299
• ### Balance Preferences with Performance in Group Role Assignment

Publication Year: 2017, Page(s):1 - 14
| | PDF (3657 KB)

Role assignment is a critical element in the role-based collaboration process. There are many factors to consider when decision makers undertake this task. Such factors include a decision maker's preferences and the team's performance. This paper proposes a series of methods, relative to these factors, to solve the group role assignment with balance problem through an association with the one clau... View full abstract»

• ### Probabilistic Regularized Extreme Learning Machine for Robust Modeling of Noise Data

Publication Year: 2017, Page(s):1 - 10
| | PDF (2416 KB)

The extreme learning machine (ELM) has been extensively studied in the machine learning field and has been widely implemented due to its simplified algorithm and reduced computational costs. However, it is less effective for modeling data with non-Gaussian noise or data containing outliers. Here, a probabilistic regularized ELM is proposed to improve modeling performance with data containing non-G... View full abstract»

• ### A Multiobjective Evolutionary Algorithm Based on Structural and Attribute Similarities for Community Detection in Attributed Networks

Publication Year: 2017, Page(s):1 - 14
| | PDF (2066 KB)

Most of the existing community detection algorithms are based on vertex connectivity. While in many real networks, each vertex usually has one or more attributes describing its properties which are often homogeneous in a cluster. Such networks can be modeled as attributed graphs, whose attributes sometimes are equally important to topological structure in graph clustering. One important challenge ... View full abstract»

• ### Aperiodic Robust Model Predictive Control for Constrained Continuous-Time Nonlinear Systems: An Event-Triggered Approach

Publication Year: 2017, Page(s):1 - 9
| | PDF (775 KB)

The event-triggered control is a promising solution to cyber-physical systems, such as networked control systems, multiagent systems, and large-scale intelligent systems. In this paper, we propose an event-triggered model predictive control (MPC) scheme for constrained continuous-time nonlinear systems with bounded disturbances. First, a time-varying tightened state constraint is computed to achie... View full abstract»

• ### POSE: Prediction-Based Opportunistic Sensing for Energy Efficiency in Sensor Networks Using Distributed Supervisors

Publication Year: 2017, Page(s):1 - 14
| | PDF (1692 KB)

This paper presents a distributed supervisory control algorithm that enables opportunistic sensing for energy-efficient target tracking in a sensor network. The algorithm called Prediction-based Opportunistic Sensing (POSE), is a distributed node-level energy management approach for minimizing energy usage. Distributed sensor nodes in the POSE network self-adapt to target trajectories by enabling ... View full abstract»

• ### A Random Walk Approach to Query Informative Constraints for Clustering

Publication Year: 2017, Page(s):1 - 12
| | PDF (1704 KB)

This paper presents a random walk approach to the problem of querying informative constraints for clustering. The proposed method is based on the properties of the commute time, that is the expected time taken for a random walk to travel between two nodes and return, on the adjacency graph of data. Commute time has the nice property of that, the more short paths connect two given nodes in a graph,... View full abstract»

• ### Set-Based Discrete Particle Swarm Optimization Based on Decomposition for Permutation-Based Multiobjective Combinatorial Optimization Problems

Publication Year: 2017, Page(s):1 - 15
| | PDF (2257 KB) |  Media

This paper studies a specific class of multiobjective combinatorial optimization problems (MOCOPs), namely the permutation-based MOCOPs. Many commonly seen MOCOPs, e.g., multiobjective traveling salesman problem (MOTSP), multiobjective project scheduling problem (MOPSP), belong to this problem class and they can be very different. However, as the permutation-based MOCOPs share the inherent similar... View full abstract»

• ### Nonrigid Point Set Registration by Preserving Local Connectivity

Publication Year: 2017, Page(s):1 - 10
| | PDF (1351 KB)

This paper is concerned with the nonrigid point set registration problem and a probability-based registration algorithm with local connectivity preservation is proposed. A unified formulation for point set registration problem is introduced and the derived energy function is composed of three parts, distance measurement item, transformation constraint item, and correspondence constraint item. In o... View full abstract»

• ### Semantic Feature Learning for Heterogeneous Multitask Classification via Non-Negative Matrix Factorization

Publication Year: 2017, Page(s):1 - 10
| | PDF (1493 KB)

Multitask learning (MTL) aims to learn multiple related tasks simultaneously instead of separately to improve the generalization performance of each task. Most existing MTL methods assumed that the multiple tasks to be learned have the same feature representation. However, this assumption may not hold for many real-world applications. In this paper, we study the problem of MTL with heterogeneous f... View full abstract»

• ### Cooperative Output Regulation of LTI Plant via Distributed Observers With Local Measurement

Publication Year: 2017, Page(s):1 - 11
| | PDF (561 KB)

Over the last decades, distributed output regulation problems have received much consideration due to its extensively applications in real world practices. Traditionally, it is assumed that each node obtains the same signal. However, an important observation is that each agent possesses different measurement due to the observability or configuration of the systems. To solve the output regulation p... View full abstract»

• ### A Flexible Terminal Approach to Sampled-Data Exponentially Synchronization of Markovian Neural Networks With Time-Varying Delayed Signals

Publication Year: 2017, Page(s):1 - 13
| | PDF (824 KB)

This paper investigates the problem of sampled-data (SD) exponentially synchronization for a class of Markovian neural networks with time-varying delayed signals. Based on the tunable parameter and convex combination computational method, a new approach named flexible terminal approach is proposed to reduce the conservatism of delay-dependent synchronization criteria. The SD subject to stochastic ... View full abstract»

• ### Discriminative Transformation Learning for Fuzzy Sparse Subspace Clustering

Publication Year: 2017, Page(s):1 - 14
| | PDF (2250 KB)

This paper develops a novel iterative framework for subspace clustering (SC) in a learned discriminative feature domain. This framework consists of two modules of fuzzy sparse SC and discriminative transformation learning. In the first module, fuzzy latent labels containing discriminative information and latent representations capturing the subspace structure will be simultaneously evaluated in a ... View full abstract»

• ### Delay-Dependent Functional Observer Design for Linear Systems With Unknown Time-Varying State Delays

Publication Year: 2017, Page(s):1 - 13;
| | PDF (1609 KB)

Partial state estimation has numerous applications in practice. Nevertheless, designing delay-dependent functional observers (FOs) for systems with unknown time delays is rigorous and still an open dilemma. This paper addresses the problem for linear time-invariant systems with state time-varying delays. The delay is assumed to be bounded in an interval with a bounded derivative. A sliding mode FO... View full abstract»

• ### Distributed Differential Evolution Based on Adaptive Mergence and Split for Large-Scale Optimization

Publication Year: 2017, Page(s):1 - 15
| | PDF (1452 KB) |  Media

Nowadays, large-scale optimization problems are ubiquitous in many research fields. To deal with such problems efficiently, this paper proposes a distributed differential evolution with adaptive mergence and split (DDE-AMS) on subpopulations. The novel mergence and split operators are designed to make full use of limited population resource, which is important for large-scale optimization. They ar... View full abstract»

• ### Privacy Preservation in Distributed Subgradient Optimization Algorithms

Publication Year: 2017, Page(s):1 - 12;
| | PDF (636 KB)

In this paper, some privacy-preserving features for distributed subgradient optimization algorithms are considered. Most of the existing distributed algorithms focus mainly on the algorithm design and convergence analysis, but not the protection of agents' privacy. Privacy is becoming an increasingly important issue in applications involving sensitive information. In this paper, we first show that... View full abstract»

• ### Output Feedback Control and Stabilization for Multiplicative Noise Systems With Intermittent Observations

Publication Year: 2017, Page(s):1 - 11
| | PDF (550 KB)

This paper mainly focuses on the optimal output feedback control and stabilization problems for discrete-time multiplicative noise system with intermittent observations. The main contributions of this paper can be concluded as follows. First, different from the previous literatures, this paper overcomes the barrier of the celebrated separation principle for stochastic control problems of multiplic... View full abstract»

• ### Towards Occlusion Handling: Object Tracking With Background Estimation

Publication Year: 2017, Page(s):1 - 15
| | PDF (4892 KB)

The appearance model of the target needs to be updated for online single object tracking. However, the variation of the observation can be caused by active appearance change of the target, or the occlusion from the background. For the former case, we should update the appearance model and for the latter, the current model should be preserved. In this paper, we distinguish these two cases and resis... View full abstract»

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

Publication Year: 2017, Page(s):1 - 13
| | PDF (3516 KB)

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»

• ### Composite Backstepping Consensus Algorithms of Leader-Follower Higher-Order Nonlinear Multiagent Systems Subject to Mismatched Disturbances

Publication Year: 2017, Page(s):1 - 12
| | PDF (1201 KB)

This paper is devoted to solving the output consensus problem of leader-follower higher-order nonlinear multiagent systems subject to mismatched disturbances. The disturbances are allowed to be in higher-order forms. First, by constructing a generalized proportional-integral observer for each follower, estimates of the disturbances and their derivatives are obtained. At the same time, a distribute... View full abstract»

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

Publication Year: 2017, Page(s):1 - 12
| | PDF (2106 KB)

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 network... View full abstract»

• ### Discriminative Joint-Feature Topic Model With Dual Constraints for WCE Classification

Publication Year: 2017, Page(s):1 - 12
| | PDF (1377 KB)

Wireless capsule endoscopy (WCE) enables clinicians to examine the digestive tract without any surgical operations, at the cost of a large amount of images to be analyzed. The main challenge for automatic computer-aided diagnosis arises from the difficulty of robust characterization of these images. To tackle this problem, a novel discriminative joint-feature topic model (DJTM) with dual constrain... View full abstract»

• ### Distributed Parametric Consensus Optimization With an Application to Model Predictive Consensus Problem

Publication Year: 2017, Page(s):1 - 12
| | PDF (795 KB)

In this paper, we study a special class of distributed convex optimization problems--distributed parametric consensus optimization problem (DPCOP), for which a two-stage optimization method including primal decomposition and distributed consensus is provided. Different from traditional distributed optimization problems driving all the local states to a common value, DPCOP aims to solve a system-wi... View full abstract»

• ### Robust Learning With Kernel Mean $p$-Power Error Loss

Publication Year: 2017, Page(s):1 - 13
| | PDF (2850 KB)

Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing. In this paper, we define a nonsecond order statistical measure in kernel space, called the kernel mean-p power error (KMPE), including the correntropic loss (C-Loss) as a special case. Some basic properties of KMPE are presented. In particular, we apply t... View full abstract»

• ### Output-Feedback Control of Unknown Linear Discrete-Time Systems With Stochastic Measurement and Process Noise via Approximate Dynamic Programming

Publication Year: 2017, Page(s):1 - 12
| | PDF (1133 KB)

This paper studies the optimal output-feedback control problem for unknown linear discrete-time systems with stochastic measurement and process noise. A dithered Bellman equation with the innovation covariance matrix is constructed via the expectation operator given in the form of a finite summation. On this basis, an output-feedback-based approximate dynamic programming method is developed, where... View full abstract»

• ### Incremental Codebook Adaptation for Visual Representation and Categorization

Publication Year: 2017, Page(s):1 - 12
| | PDF (2354 KB)

The bag-of-visual-words model is widely used for visual content analysis. For visual data, the codebook plays an important role for efficient representation. However, the codebook has to be relearned with the changes of training images. Once the codebook is changed, the encoding parameters of local features have to be recomputed. To alleviate this problem, in this paper, we propose an incremental ... 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