Finite‐time stability of a class of oscillating systems with two delays

Cao, Xiaokai; Wang, JinRong

doi:10.1002/mma.4943

Cited by 36 publications

(22 citation statements)

References 32 publications

(59 reference statements)

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“…[1][2][3][4][5][6][7][8][9] However, these methods depend on the known upper bounds of unknown time-delays in inputs or states. Hence, study for nonlinear time-delayed systems is significative both in theory and in practice.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a large number of control strategies and stability analysis methods based on Lyapunov-Krasovskii function, linear matrix inequality, backstepping technique, and supervisory control were proposed for time-delayed systems. [1][2][3][4][5][6][7][8][9] However, these methods depend on the known upper bounds of unknown time-delays in inputs or states.…”

Section: Introductionmentioning

confidence: 99%

“…Conventional nonlinear system control approaches suffer from discontented performance resulted from existed input nonlinearities, parametric uncertainties, external disturbances, and time-varying delays. 6,[10][11][12][13][14] Takagi-Sugeno (T-S) fuzzy logic system is a wonderfully handy tool that has been proposed by Takagi and Sugeno, 15 which presents nonlinear systems through a group of "IF-THEN" rules via "blending" all subsystems with membership functions, and it can uniformly approximate any continuous function on a compact set with random accuracy. 16 It should lay great emphasis on T-S fuzzy system, which has better learning capability in modeling nonlinear system with uncertainties than other types of fuzzy systems.…”

Section: Introductionmentioning

confidence: 99%

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Composite learning fuzzy synchronization for incommensurate fractional‐order chaotic systems with time‐varying delays

Zhou

Liu

Cao

et al. 2019

Adaptive Control & Signal

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This paper presents a composite learning fuzzy control to synchronize two different uncertain incommensurate fractional-order time-varying delayed chaotic systems with unknown external disturbances and mismatched parametric uncertainties via the Takagi-Sugeno fuzzy method. An adaptive controller together with fractional-order composite learning laws is designed based on both a parallel distributed compensation technology and a fractional Lyapunov criterion. The boundedness of all variables in the closed-loop system and the Mittag-Leffler stability of tracking error can be guaranteed. T-S fuzzy systems are provided to tackle unknown nonlinear functions. The distinctive features of the proposed approach consist in the following: (1) a supervisory control law is designed to compensate the lumped disturbances; (2) both the prediction error and the tracking error are used to estimate the unknown fuzzy system parameters; (3) parameter convergence can be ensured by an interval excitation condition. Finally, the feasibility of the proposed control strategy is demonstrated throughout an illustrative example.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Composite learning fuzzy synchronization for incommensurate fractional‐order chaotic systems with time‐varying delays

Zhou

Liu

Cao

et al. 2019

Adaptive Control & Signal

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“…Next, stability and controllability problems of linear delay differential equations were studied extensively in [15][16][17]. For the literature on the related topic of linear fractional delay equations of Caputo and Riemann-Liouville type, we refer the reader to [18][19][20][21][22][23][24][25]. However, we find that there exists very limited work on the representation of solutions of fractional order delay differential equations of Hadamard type, even for linear case.…”

Section: Introductionmentioning

confidence: 99%

Representation of solution for a linear fractional delay differential equation of Hadamard type

2019

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“…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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Finite‐time stability of a class of oscillating systems with two delays

Cited by 36 publications

References 32 publications

Composite learning fuzzy synchronization for incommensurate fractional‐order chaotic systems with time‐varying delays

Composite learning fuzzy synchronization for incommensurate fractional‐order chaotic systems with time‐varying delays

Representation of solution for a linear fractional delay differential equation of Hadamard type

Research of animals image semantic segmentation based on deep learning

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