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IEEE Transactions on Cybernetics

Dong Wang, Jianjie Yin, Wei Wang
In this paper, a distributed randomized gradient-free optimization protocol of multiagent systems over weight-unbalanced digraphs described by row-stochastic matrices is proposed to solve a distributed constrained convex optimization problem. Each agent possesses its local nonsmooth, but Lipschitz continuous, objective function and assigns the weight to information gathered from in-neighbor agents to update its decision state estimation, which is applicable and straightforward to implement. In addition, our algorithm relaxes the requirements of diminishing step sizes to only a nonsummable condition under convex bounded constraint sets...
January 11, 2019: IEEE Transactions on Cybernetics
Taotao Lai, Hanzi Wang, Yan Yan, Tat-Jun Chin, Jin Zheng, Bo Li
The performance of many robust model fitting techniques is largely dependent on the quality of the generated hypotheses. In this paper, we propose a novel guided sampling method, called accelerated guided sampling (AGS), to efficiently generate the accurate hypotheses for multistructure model fitting. Based on the observations that residual sorting can effectively reveal the data relationship (i.e., determine whether two data points belong to the same structure), and keypoint matching scores can be used to distinguish inliers from gross outliers, AGS effectively combines the benefits of residual sorting and keypoint matching scores to efficiently generate accurate hypotheses via information theoretic principles...
January 11, 2019: IEEE Transactions on Cybernetics
Wentao Wang, Nan Niu, Mounifah Alenazi, Juha Savolainen, Zhendong Niu, Jing-Ru C Cheng, Li Da Xu
Complementarity between activities reveals that doing any one of them increases the returns to doing the others. In other words, complementarity leads to the synergistic effect that the whole is greater than the sum of its parts. Identifying and exploiting complementarity can benefit many cybernetic activities, where human-machine interactions are inherent and dominant. One such activity is requirements tracing that helps stakeholders to track the status of their goals. Although various kinds of support for human analysts in requirements tracing have been proposed, little is known about the nature of complementarity when different tracing practices are involved...
January 11, 2019: IEEE Transactions on Cybernetics
Yong Xu, Zheng-Guang Wu, Ya-Jun Pan
This paper adopts two different approaches, the small-gain technique and the integral quadratic constraints (IQCs), to investigate the synchronization problem of coupled harmonic oscillators (CHOs) via an event-triggered control strategy in a directed graph. First, a novel control protocol is proposed such that every state signal of the CHO decides when to exchange information with its neighbors asynchronously. Then, the resulting closed-loop system based on the designed control protocol is converted into a feedback interconnection of a linear system and a bounded operator, and the stable condition of the feedback interconnection is presented by employing the small-gain technique...
January 11, 2019: IEEE Transactions on Cybernetics
Edmond Q Wu, Gui-Rong Zhou, Li-Min Zhu, Chuan-Feng Wei, He Ren, Richard S F Sheng
How to track the attention of the pilot is a huge challenge. We are able to capture the pupil status of the pilot and analyze their anomalies and judge the attention of the pilot. This paper proposes a new approach to solve this problem through the integration of spherical Haar wavelet transform and deep learning methods. First, considering the application limitations of Haar wavelet and other wavelets in spherical signal decomposition and reconstruction, a feature learning method based on the spherical Haar wavelet is proposed...
January 11, 2019: IEEE Transactions on Cybernetics
Jie Ding, Changyun Wen, Guoqi Li, Zhenghua Chen
Key nodes are the nodes connected with a given number of external source controllers that result in minimal control cost. Finding such a subset of nodes is a challenging task since it impossible to list and evaluate all possible solutions unless the network is small. In this paper, we approximately solve this problem by proposing three algorithms step by step. By relaxing the Boolean constraints in the original optimization model, a convex problem is obtained. Then inexact alternating direction method of multipliers (IADMMs) is proposed and convergence property is theoretically established...
January 9, 2019: IEEE Transactions on Cybernetics
Zheng Zhang, Shiming Chen, Housheng Su
In this paper, the scaled consensus of multiagent systems (MASs) with second-order nonlinear dynamics and time-varying delays is investigated, where agents are supposed to aperiodically communicate with each other under a directed graph at some disconnected time intervals. Different from the existing works, we consider the case where time-varying delays exist in both nonlinear dynamics and communication networks. First, to address this problem, we propose a novel scaled consensus protocol. Second, using Lyapunov theory and graph theory, it is proved that under mild conditions, MASs with second-order nonlinear dynamics exponentially reach scaled consensus...
January 9, 2019: IEEE Transactions on Cybernetics
Chenwei Deng, Shuigen Wang, Alan C Bovik, Guang-Bin Huang, Baojun Zhao
Noise that afflicts natural images, regardless of the source, generally disturbs the perception of image quality by introducing a high-frequency random element that, when severe, can mask image content. Except at very low levels, where it may play a purpose, it is annoying. There exist significant statistical differences between distortion-free natural images and noisy images that become evident upon comparing the empirical probability distribution histograms of their discrete wavelet transform (DWT) coefficients...
January 8, 2019: IEEE Transactions on Cybernetics
Zhao Kang, Haiqi Pan, Steven C H Hoi, Zenglin Xu
Learning graphs from data automatically have shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause the learned graph to be inexact or unreliable. In this paper, we propose a novel robust graph learning scheme to learn reliable graphs from the real-world noisy data by adaptively removing noise and errors in the raw data. We show that our proposed model can also be viewed as a robust version of manifold regularized robust principle component analysis (RPCA), where the quality of the graph plays a critical role...
January 8, 2019: IEEE Transactions on Cybernetics
Shuai Liu, Zidong Wang, Guoliang Wei, Maozhen Li
In this paper, the distributed set-membership filtering problem is dealt with for a class of time-varying multirate systems in sensor networks with the communication protocol. For relieving the communication burden, the round-Robin (RR) protocol is exploited to orchestrate the transmission order, under which each sensor node only broadcasts partial information to both the corresponding local filter and its neighboring nodes. In order to meet the practical transmission requirements as well as reduce communication cost, the multirate strategy is proposed to govern the sampling/update rate of the plant, the sensors, and the filters...
January 8, 2019: IEEE Transactions on Cybernetics
Shanling Dong, Mei Fang, Peng Shi, Zheng-Guang Wu, Dan Zhang
In this paper, the problem of asynchronous output feedback control is investigated for a class of Takagi-Sugeno fuzzy switched systems subject to intermittent measurements. The Bernoulli process is employed to model the phenomenon of stochastic intermittent measurements. Based on the hidden Markov model and output measurements, an asynchronous controller is designed. Then, sufficient conditions for the existence of an asynchronous controller are proposed, which ensure the stochastic stability of the closed-loop system with desired extended dissipative performance...
January 4, 2019: IEEE Transactions on Cybernetics
Zhixi Feng, Shuyuan Yang, Min Wang, Licheng Jiao
Most of the available graph-based semisupervised hyperspectral image classification methods adopt the cluster assumption to construct a Laplacian regularizer. However, they sometimes fail due to the existence of mixed pixels whose recorded spectra are a combination of several materials. In this paper, we propose a geometric low-rank Laplacian regularized semisupervised classifier, by exploring both the global spectral geometric structure and local spatial geometric structure of hyperspectral data. A new geometric regularized Laplacian low-rank representation (GLapLRR)-based graph is developed to evaluate spectral-spatial affinity of mixed pixels...
January 4, 2019: IEEE Transactions on Cybernetics
Chenwei Deng, Yuqi Han, Baojun Zhao
In real-time applications, a fast and robust visual tracker should generally have the following important properties: 1) feature representation of an object that is not only efficient but also has a good discriminative capability and 2) appearance modeling which can quickly adapt to the variations of foreground and backgrounds. However, most of the existing tracking algorithms cannot achieve satisfactory performance in both of the two aspects. To address this issue, in this paper, we advocate a novel and efficient visual tracker by exploiting the excellent feature learning and classification capabilities of an emerging learning technique, that is, extreme learning machine (ELM)...
January 3, 2019: IEEE Transactions on Cybernetics
Syed Ali Asad Rizvi, Zongli Lin
In this paper, we propose a model-free solution to the linear quadratic regulation (LQR) problem of continuous-time systems based on reinforcement learning using dynamic output feedback. The design objective is to learn the optimal control parameters by using only the measurable input-output data, without requiring model information. A state parametrization scheme is presented which reconstructs the system state based on the filtered input and output signals. Based on this parametrization, two new output feedback adaptive dynamic programming Bellman equations are derived for the LQR problem based on policy iteration and value iteration (VI)...
January 3, 2019: IEEE Transactions on Cybernetics
Yinghua Shen, Witold Pedrycz, Xianmin Wang
In this paper, we propose a gradient-based method to approximate a fuzzy set through a trapezoidal fuzzy set (TFS). By adding some constraints in the formulated optimization problem, the major characteristics of the fuzzy set such as the core, the major part of the support, and the shape of the membership function could be preserved; also the form of the optimized result as a TFS is guaranteed. We regard the optimized TFS as the ``skeleton'' (blueprint) of the original fuzzy set. Based on this skeleton, we further extend the TFS to a higher type, that is, an interval type-2 TFS (IT2 TFS), so that more information about the original fuzzy set could be captured but the number of the parameters used to describe the original fuzzy set is still maintained low (nine parameters are required for an IT2 TFS)...
January 3, 2019: IEEE Transactions on Cybernetics
Zheng-Guang Wu, Shanling Dong, Peng Shi, Dan Zhang, Tingwen Huang
This paper is concerned with the problem of asynchronous and reliable filter design with performance constraint for nonlinear Markovian jump systems which are modeled as a kind of Takagi-Sugeno fuzzy switched systems. The nonstationary Markov chain is adopted to represent the asynchronous situation between the designed filter and the considered system. By using the mode-dependent Lyapunov function approach and the relaxation matrix technique, a sufficient condition is proposed to ensure the filtering error system, which is a dual randomly switched system, is stochastically stable and satisfies a given l₂-l∞ performance index simultaneously...
January 3, 2019: IEEE Transactions on Cybernetics
Tinghui Ouyang, Witold Pedrycz, Orion F Reyes-Galaviz, Nick J Pizzi
The study is concerned with a description of large numeric data with the aid of building a limited collection of representative information granules with the objective of capturing the structure of the original data. The proposed development scheme consists of two steps. First, a clustering algorithm characterized by high flexibility of coping with the diverse geometry of data structure and efficient computational overhead is invoked. At the second step, a clustering algorithm applied to the clusters already formed during the first phase, yielding a collection of numeric prototypes is involved and the numeric prototypes produced there are then generalized into their granular prototypes...
January 1, 2019: IEEE Transactions on Cybernetics
Hamidou Tembine
This paper presents an interplay between deep learning and game theory. It models basic deep learning tasks as strategic games. Then, distributionally robust games and their relationship with deep generative adversarial networks (GANs) are presented. To achieve a higher order convergence rate without using a second derivative of the objective function, a Bregman discrepancy is used to construct a speed-up deep learning. Each player has a continuous action space which corresponds to weight space and aims to learn his/her optimal strategy...
January 1, 2019: IEEE Transactions on Cybernetics
Victor Adrian Sosa Hernandez, Oliver Schutze, Hao Wang, Andre Deutz, Michael Emmerich
In this paper, we propagate the use of a set-based Newton method that enables computing a finite size approximation of the Pareto front (PF) of a given twice continuously differentiable bi-objective optimization problem (BOP). To this end, we first derive analytically the Hessian matrix of the hypervolume indicator, a widely used performance indicator for PF approximation sets. Based on this, we propose the hypervolume Newton method (HNM) for hypervolume maximization of a given set of candidate solutions. We first address unconstrained BOPs and focus further on first attempts for the treatment of inequality constrained problems...
December 25, 2018: IEEE Transactions on Cybernetics
Yifan Shi, Zhiwen Yu, C L Philip Chen, Jane You, Hau-San Wong, Yide Wang, Jun Zhang
Clustering ensemble (CE) takes multiple clustering solutions into consideration in order to effectively improve the accuracy and robustness of the final result. To reduce redundancy as well as noise, a CE selection (CES) step is added to further enhance performance. Quality and diversity are two important metrics of CES. However, most of the CES strategies adopt heuristic selection methods or a threshold parameter setting to achieve tradeoff between quality and diversity. In this paper, we propose a transfer CES (TCES) algorithm which makes use of the relationship between quality and diversity in a source dataset, and transfers it into a target dataset based on three objective functions...
December 25, 2018: IEEE Transactions on Cybernetics
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