Event-triggered communication for synchronization of Markovian jump delayed complex networks with partially unknown transition rates

Zhou, Jiamu; Dong, Hailing; Feng, Jianwen

doi:10.1016/j.amc.2016.06.039

Cited by 41 publications

(21 citation statements)

References 40 publications

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“…i. In contrast to the works of Zhou et al 30 and Dai et al, 31 in this paper, the presented control method not can only ensure that the synchronization errors are convergent to a prespecified bounded set but also can guarantee the transient synchronization performance. ii.…”

Section: Introductionmentioning

confidence: 80%

“…Remark 3. It is worth noting that the results proposed in the works of Zhou et al 30 and Dai et al 33 are that the numbers of information updates have been decreased among the nodes of networks, the transient problems of synchronization course are not considered. In the work of Fan and Li, 36 although the transient and steady-state problems of synchronization course have been solved, the communication among nodes of the networks is continuous.…”

Section: Convergence Analysismentioning

confidence: 99%

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Bounded synchronization of complex dynamical networks with prescribed performance via event‐based transmission strategy

Fan

Wei

2019

Adaptive Control & Signal

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This paper addresses a novel prescribed performance synchronization of complex dynamical networks by means of event-triggered communication protocols.With the designed controller and proposed even-triggered communication strategy, this paper have advantages of guaranteeing the transient and steady-state performance of complex dynamic networks and avoiding the continuous communication of networks for reducing the number of communications and saving the network resources. In addition, the Zeno behavior is avoided in the networks. At last, the effectiveness of the theoretic results obtained is verified via the application in the complex dynamical network with Chua's circuit.As we all know, in the past several years, with the rapid development of digital computers, the event-based communication schemes have received significant attention in signal processing. 24,25 Considering the event-based communication schemes, the information transmission from the sensors to the networks is permitted only when a particular condition of the event trigger function holds. Therefore, different from the common continuous transmission of information, the event-based communication mechanism can reduce the waste of network resources. To the present, the event-based communication synchronization problems of CNs have yielded considerable results. [26][27][28] Concretely, in the work of Li et al, 29 the authors studied the event-triggered intermittent communication synchronization problems for general directed CNs, which proposed a unified framework for synchronization and designed a newfangled distributed event-triggering sampling control method. For the Markovian jump CDNs with delayed, in the work of Zhou et al, 30 the researchers studied the exponential synchronization problems by proposing a novel randomly occurring event-based control method. The synchronization problems of CNs were investigated in the work of Dai et al 31 via distributed event-triggered communication method. Although the literary works mentioned earlier have contributed to the synchronization problem of event-driven communication in complex dynamic networks, the significance of the transient problem of synchronization course has not been considered.In fact, the transient performance of synchronization process is an important factor to consider. [32][33][34] In traditional synchronization course of CNs, the synchronization errors are testified to converge within a bounded set near zero, whose size decides by the designed parameters and some unknown bounded terms. Then, the prescribed performance control method 32 is raised to ensure that the tracking errors gather to a predefined bound accompanying with the convergence rate no less than a certain value. So far, the prescribed performance issues 32 have been expanded to the multiagent systems 35 and the complex dynamical (networks) systems. 36 However, the results proposed in related works 32-37 do not consider the event-triggered communication problem. As far as our knowledge is concerned, up to the present, no resul...

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Section: Introductionmentioning

confidence: 80%

Section: Convergence Analysismentioning

confidence: 99%

Bounded synchronization of complex dynamical networks with prescribed performance via event‐based transmission strategy

Fan

Wei

2019

Adaptive Control & Signal

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“…Theorem 2. Under Assumptions 1 to 3, the followers (1) can track the leader (2) in the mean-square sense with bounded residual errors utilizing the controller (33) if there exist matrices P v > 0,…”

Section: Consensus Based On Output Feedbackmentioning

confidence: 99%

“…The consensus of multiagent systems over uncertain switching was investigated by other authors. 12,33,34 The consensus of linear multiagent systems over uncertain Markov switching topology was studied in the work of Savino et al 12 The exponential synchronization problems for nonlinear multiagent systems with uncertain switching topologies were investigated by Zhou et al, 33 in which the nonlinear parts satisfy the global Lipschitz condition.…”

Section: Introductionmentioning

confidence: 99%

Adaptive cooperative tracking control of uncertain nonlinear multiagent systems with uncertain Markov switching communication graphs

Xing

Deng

2019

Adaptive Control & Signal

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This paper deals with the cooperative tracking problem of nonlinear multiagent systems. Compared with the existing works, both the uncertainties in model and switching topology are considered. Two control laws, the adaptive distributed controller based on state information and the adaptive distributed controller based on output information, are proposed using the neural networks.The advantage of the proposed controller is that we no longer require the exact knowledge of follower agents' parameters and the precise switching signal of communication topology by taking advantages of neural networks approximation and the property of transition probabilities. It is proved that all followers can track the leader with permitted bounded errors under the proposed controller.An illustration is given to testify the efficacy of the proposed approach. KEYWORDS adaptive cooperative control, Markov switching, nonlinear multiagent systems, uncertain parameter Abbreviations: TPs, transition probabilities 1506

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“…Websites such as Kaggle plays a great role in promoting the semantic segmentation of images in practical applications and they are good at semantic editing or based on a more realistic scenario, such as MIT and satellite images, etc, for semantic segmentation in the context of deep learning. There are also a large number of valuable research results, which a large number of researchers have done semantic segmentation and realistic applications based on deep learning. Besides, the deep learning techniques have been applied to various fields of image processing such as other works, making a great breakthrough in traditional image processing.…”

Section: Introductionmentioning

confidence: 99%

Research of animals image semantic segmentation based on deep learning

Liu

et al. 2018

Concurrency and Computation

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It is imperative for us to develop the technology of image semantic segmentation with the increasing demand in the image processing. Nowadays, the development of deep learning is of great significance to the improvement of image segmentation. Furthermore, the paper discussed the relationship between image semantic segmentation and animal image research based on the actual situation, and found that animal image processing technology plays a more important role in the field of protecting precious animals. The end-to-end network training of this paper is consisted of Fully Convolutional Network (FCN) for the front end and Conditional Random Fields as Recurrent Neural Networks (CRF-RNN) for the back end via comparing a variety of research methods. The experiments achieved desired outcome for the semantic segmentation of animal images by utilizing Caffe deep learning framework and explained the implementation details from the aspects of training and testing. KEYWORDS Caffe, CRF-RNN, FCN, image semantic segmentation INTRODUCTIONTraditionally, the segmentation of the image is only directly on the surface layer, such as the appearance features based on texture, color, and shape, and they are separated after aggregating the similar features. Among them, the segmentation method based on regions, because the selection of seed points will lead to low efficiency of the algorithm, the segmented results have a certain flaw and can not reach the expected effect. However, the histogram-based threshold segmentation method ignores certain image space information, and it is very likely to segment a discontinuous and meaningless regions, making the segmentation result less satisfying. Therefore, with the demand for image segmentation technology, the semantic segmentation is indispensable.In semantic segmentation, the method of deep learning can be well escaped from the traditional processing methods, and the limitation of using only the underlying features is eliminated, making process in the level of understanding for image. The deep learning framework of Caffe 1 can handle semantic segmentation well. As the number of treasured animals continues to decline, the application of science and technology to protect them is imminent. In particular, it is necessary to collect a set of quality and distinctive animal image data for a specific animal, and the collection of a large number of data sets depends not only on human resources but also requires the support of the relevant sensors, cameras, and other hardware in the wild places it can automatically detect in the field or other places. One solution is to establish a corresponding index for the existing animal picture database. For the protected animal, it can use image processing technology to quickly identify for these animals, and then semantic segmentation is exploited to make a good recognition of animal images. This will not only increase the efficiency of the staff, but reduce the situation in which Concurrency Computat Pract Exper. 2020;32:e4892. wileyonlinelibrary.com/journal/cp...

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Event-triggered communication for synchronization of Markovian jump delayed complex networks with partially unknown transition rates

Cited by 41 publications

References 40 publications

Bounded synchronization of complex dynamical networks with prescribed performance via event‐based transmission strategy

Bounded synchronization of complex dynamical networks with prescribed performance via event‐based transmission strategy

Adaptive cooperative tracking control of uncertain nonlinear multiagent systems with uncertain Markov switching communication graphs

Research of animals image semantic segmentation based on deep learning

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