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IEEE Transactions on Cybernetics

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https://www.readbyqxmd.com/read/30207977/observer-based-consensus-control-for-discrete-time-multiagent-systems-with-coding-decoding-communication-protocol
#1
Licheng Wang, Zidong Wang, Guoliang Wei, Fuad E Alsaadi
In this paper, the consensus control problem is investigated for a class of discrete-time networked multiagent systems (MASs) with the coding-decoding communication protocol (CDCP). Under a directed communication topology, an observer-based control scheme is proposed for each agent by utilizing the relative measurement outputs between the agent itself and its neighboring ones. The signal delivery is in a digital manner, which means that only the sequence of finite coded signals is sent from the observer to the controller...
September 10, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30207976/recurrent-broad-learning-systems-for-time-series-prediction
#2
Meiling Xu, Min Han, C L Philip Chen, Tie Qiu
The broad learning system (BLS) is an emerging approach for effective and efficient modeling of complex systems. The inputs are transferred and placed in the feature nodes, and then sent into the enhancement nodes for nonlinear transformation. The structure of a BLS can be extended in a wide sense. Incremental learning algorithms are designed for fast learning in broad expansion. Based on the typical BLSs, a novel recurrent BLS (RBLS) is proposed in this paper. The nodes in the enhancement units of the BLS are recurrently connected, for the purpose of capturing the dynamic characteristics of a time series...
September 10, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30207975/delay-distribution-dependent-h%C3%A2-state-estimation-for-discrete-time-memristive-neural-networks-with-mixed-time-delays-and-fading-measurements
#3
Hongjian Liu, Zidong Wang, Bo Shen, Hongli Dong
This paper addresses the H∞ state estimation issue for a sort of memristive neural networks in the discrete-time setting under randomly occurring mixed time-delays and fading measurements. The main purpose of the addressed issue is to propose a state estimator design algorithm that ensures the error dynamics of the state estimation to be stochastically stable with a prespecified H∞ disturbance attenuation index. We put forward certain switching functions to account for the discrete-time yet state-dependent characteristics of the memristive connection weights...
September 10, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30207974/adaptive-event-triggered-transmission-scheme-and-h%C3%A2-filtering-co-design-over-a-filtering-network-with-switching-topology
#4
Hao Zhang, Zhuping Wang, Huaicheng Yan, Fuwen Yang, Xue Zhou
This paper addresses the distributed adaptive event-triggered H∞ filtering problem for a class of sector-bounded nonlinear system over a filtering network with time-varying and switching topology. Both topology switching and adaptive event-triggered mechanisms (AETMs) between filters are simultaneously considered in the filtering network design. The communication topology evolves over time, which is assumed to be subject to a nonhomogeneous Markov chain. In consideration of the limited network bandwidth, AETMs have been used in the information transmission from the sensor to the filter as well as the information exchange among filters...
September 10, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30207973/evolutionary-many-objective-algorithm-using-decomposition-based-dominance-relationship
#5
Lei Chen, Hai-Lin Liu, Kay Chen Tan, Yiu-Ming Cheung, Yuping Wang
Decomposition-based evolutionary algorithms have shown great potential in many-objective optimization. However, the lack of theoretical studies on decomposition methods has hindered their further development and application. In this paper, we first theoretically prove that weight sum, Tchebycheff, and penalty boundary intersection decomposition methods are essentially interconnected. Inspired by this, we further show that highly customized dominance relationship can be derived from decomposition for any given decomposition vector...
September 10, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30207972/output-containment-control-for-heterogeneous-linear-multiagent-systems-with-fixed-and-switching-topologies
#6
Jiahu Qin, Qichao Ma, Xinghuo Yu, Yu Kang
In this paper, we investigate the output containment control problem for a network of heterogeneous linear multiagent systems. The control target is to drive the outputs of the followers into the convex hull spanned by the leaders. To this end, we first derive a necessary condition imposed on both system dynamics and network topology from the viewpoint of internal model principle. Then, based on the necessary condition, we utilize a dynamic controller to drive the outputs of the leaders and followers to track the reference trajectories to achieve containment exponentially...
September 10, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30207971/edge-convergence-problems-on-signed-networks
#7
Mingjun Du, Baoli Ma, Deyuan Meng
This paper focuses on characterizing edge dynamics of signed networks subject to both cooperative and antagonistic interactions and copes with the state convergence problems of the resulting edge systems. To represent the two competitive classes of interactions that emerge in signed networks, signed digraphs are adopted and the relevant edge Laplacian matrices are introduced, with which an edge-based distributed protocol is presented. The relation between the edge Laplacian matrix and the structural balance (or unbalance) of a signed digraph is disclosed by taking advantage of properties of undirected cycles...
September 10, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30207970/finite-horizon-optimal-consensus-control-for-unknown-multiagent-state-delay-systems
#8
Huaipin Zhang, Ju H Park, Dong Yue, Xiangpeng Xie
This paper investigates finite-horizon optimal consensus control problem for unknown multiagent systems with state delays. It is well known that optimal consensus control is the solutions to the coupled Hamilton-Jacobi-Bellman (HJB) equations. An off-policy reinforcement learning (RL) algorithm is developed to learn the two-stage optimal consensus solutions to the coupled time-varying HJB equations using the measurable state data instead of the knowledge of the state-delayed system dynamics. Subsequently, for each agent, a single critic neural network (NN) is utilized to approximate the time-varying cost function and help to calculate optimal consensus control policy...
September 10, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30207978/automatic-leader-follower-persistent-formation-generation-with-minimum-agent-movement-in-various-switching-topologies
#9
Dengxiu Yu, C L Philip Chen
This paper presents the generation strategy, motion planning, and switching topologies of a distance-based leader-follower relation-invariable persistent formation (RIPF) of multiagent systems (MASs). An efficient algorithm is designed to find out if a persistent formation can be generated from a rigid graph. Derived from the properties of a rigid graph, the algorithm to generate RIPF from any initial location is presented. In order to generate different RIPFs in the switching topology, state and transition matrices are introduced...
September 6, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30188844/incremental-class-learning-for-hierarchical-classification
#10
Ju-Youn Park, Jong-Hwan Kim
Objects can be described in hierarchical semantics, and people also perceive them this way. It leads to the need for hierarchical classification in machine learning. On the other hand, when a new data that belongs to a new class is given, the existing classification methods should be retrained for all data including the new data. To deal with these issues, we propose an adaptive resonance theory-supervised predictive mapping for hierarchical classification (ARTMAP-HC) network that allows incremental class learning for raw data without normalization in advance...
September 5, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30188843/evaluation-of-gaze-tracking-calibration-for-longitudinal-biomedical-imaging-studies
#11
Pierre Chatelain, Harshita Sharma, Lior Drukker, Aris T Papageorghiou, J Alison Noble
Gaze tracking is a promising technology for studying the visual perception of clinicians during image-based medical exams. It could be used in longitudinal studies to analyze their perceptive process, explore human-machine interactions, and develop innovative computer-aided imaging systems. However, using a remote eye tracker in an unconstrained environment and over time periods of weeks requires a certain guarantee of performance to ensure that collected gaze data are fit for purpose. We report the results of evaluating eye tracking calibration for longitudinal studies...
September 5, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30183654/event-triggered-consensus-of-homogeneous-and-heterogeneous-multiagent-systems-with-jointly-connected-switching-topologies
#12
Bin Cheng, Xiangke Wang, Zhongkui Li
This paper investigates the distributed event-based consensus problem of switching networks satisfying the jointly connected condition. Both the state consensus of homogeneous linear networks and the output consensus of heterogeneous networks are studied. Two kinds of event-based protocols based on local sampled information are designed, without the need to solve any matrix equation or inequality. Theoretical analysis indicates that the proposed event-based protocols guarantee the achievement of consensus and the exclusion of Zeno behaviors for jointly connected undirected switching graphs...
September 3, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30183652/adaptive-fuzzy-quantized-control-for-nonlinear-systems-with-hysteretic-actuator-using-a-new-filter-connected-quantizer
#13
Honghui Wu, Zhi Liu, Yun Zhang, C L Philip Chen
This paper aims at the issue of adaptive fuzzy quantized control for a class of uncertain nonlinear systems preceded by unknown actuator hysteresis. One challenging problem that obstructs the development of the control scheme is that the direct application of the quantized signal containing high-frequency components to the hysteretic actuator will lead to system performance deterioration. To resolve this challenge, we propose a filter-connected quantizer in which a hysteretic quantizer is employed to reduce the communication rate and an adaptive high-cut filter is designed to smooth the hysteresis input...
September 3, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30183651/a-deep-evaluator-for-image-retargeting-quality-by-geometrical-and-contextual-interaction
#14
Bin Jiang, Jiachen Yang, Qinggang Meng, Baihua Li, Wen Lu
An image is compressed or stretched during the multidevice displaying, which will have a very big impact on perception quality. In order to solve this problem, a variety of image retargeting methods have been proposed for the retargeting process. However, how to evaluate the results of different image retargeting is a very critical issue. In various application systems, the subjective evaluation method cannot be applied on a large scale. So we put this problem in the accurate objective-quality evaluation. Currently, most of the image retargeting quality assessment algorithms use simple regression methods as the last step to obtain the evaluation result, which are not corresponding with the perception simulation in the human vision system (HVS)...
September 3, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30183653/a-learning-based-hierarchical-control-scheme-for-an-exoskeleton-robot-in-human-robot-cooperative-manipulation
#15
Mingdi Deng, Zhijun Li, Yu Kang, C L Philip Chen, Xiaoli Chu
Exoskeleton robots can assist humans to perform activities of daily living with little effort. In this paper, a hierarchical control scheme is presented which enables an exoskeleton robot to achieve cooperative manipulation with humans. The control scheme consists of two layers. In low-level control of the upper limb exoskeleton robot, an admittance control scheme with an asymmetric barrier Lyapunov function-based adaptive neural network controller is proposed to enable the robot to be back drivable. In order to achieve high-level interaction, a strategy for learning human skills from demonstration is proposed by utilizing Gaussian mixture models, which consists of the learning and reproduction phase...
August 31, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30176619/some-necessary-and-sufficient-conditions-for-synchronization-of-second-order-interconnected-networks
#16
Yuting Feng, Zhisheng Duan, Yuezu Lv, Wei Ren
This paper presents some necessary and sufficient conditions for the synchronization of second-order interconnected networks, where fixed inner-linked connections with information communication exist. First, a novel derivation of the conditions for a second-order polynomial with complex coefficients to be Hurwitz is provided. Based on this, a sufficient and necessary condition is proposed for the synchronization of the coupled complex network. Next, the design method of the synchronization protocol is constructively given, where the upper and lower bounds of the control gains are obtained through the properties of the polynomial...
August 30, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30176618/shared-gaussian-process-latent-variable-model-for-incomplete-multiview-clustering
#17
Ping Li, Songcan Chen
These days, many multiview learning methods have been proposed by integrating the complementary information of multiple views and can significantly improve the performance of machine learning tasks comparing with single-view learning methods. However, most of these methods fail to learn better models when the multiview data are unpaired or partially paired or incomplete or partially complete. Although some previous attempts have been made to address these problems, these methods often lead to poor results when dealing with incomplete multiview data that contain a relatively large number of missing instances...
August 30, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30176617/a-common-topic-transfer-learning-model-for-crossing-city-poi-recommendations
#18
Dichao Li, Zhiguo Gong, Defu Zhang
With the popularity of location-aware devices (e.g., smart phones), large amounts of location-based social media data (i.e., user check-in data) are generated, which stimulate plenty of works on personalized point of interest (POI) recommendations using machine learning techniques. However, most of the existing works could not recommend POIs in a new city to a user where the user and his/her friends have never visited before. In this paper, we propose a common topic transfer learning graphical model-the common-topic transfer learning model (CTLM)-for crossing-city POI recommendations...
August 30, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30176616/reliable-link-inference-for-network-data-with-community-structures
#19
Lijia Ma, Jianqiang Li, Qiuzhen Lin, Maoguo Gong, Carlos A Coello Coello, Zhong Ming
Complex systems are often characterized by complex networks with links and entities. However, in many complex systems such as protein-protein interaction networks, recommender systems, and online communities, their links are hard to reveal directly, but they can be inaccurately observed by multiple data collection platforms or by a data collection platform at different times. Then, the links of the systems are inferred by the integration of the collected observations. As those data collection platforms are usually distributed over a large area and in different fields, their observations are unreliable and sensitive to the potential structures of the systems...
August 30, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/30183650/boosting-cooperative-coevolution-for-large-scale-optimization-with-a-fine-grained-computation-resource-allocation-strategy
#20
Zhigang Ren, Yongsheng Liang, Aimin Zhang, Yang Yang, Zuren Feng, Lin Wang
Cooperative coevolution (CC) has shown great potential for solving large-scale optimization problems (LSOPs). However, traditional CC algorithms often waste part of the computation resource (CR) as they equally allocate CR among all subproblems. The recently developed contribution-based CC algorithms improve the traditional ones to a certain extent by adaptively allocating CR according to some heuristic rules. Different from existing works, this paper explicitly constructs a mathematical model for the CR allocation (CRA) problem in CC and proposes a novel fine-grained CRA (FCRA) strategy by fully considering both the theoretically optimal solution of the CRA model and the evolution characteristics of CC...
August 27, 2018: IEEE Transactions on Cybernetics
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