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H∞ stability conditions for delayed neural networks with external disturbances and norm-bounded uncertainties:Delay independent and dependent criteria

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Science China(Information Sciences) 2011年第8期54卷 1691-1701页

作者： Choon Ki AHN Department of Automotive Engineering Seoul National University of Science & Technology

In this paper,we propose new delay independent and dependent H ∞ stability conditions for delayed neural networks with external disturbances and norm-bounded *** conditions are presented to not only guarantee the asymptotical stability but also reduce the effect of external disturbance to an Hnorm constraint. The proposed conditions are represented by linear matrix inequalities （LMIs）.Optimal Hnorm bounds are obtained easily by solving convex problems in terms of *** applicability of these conditions is illustrated by numerical examples.

关键词： H_∞ analysis delayed neural networks uncertainty disturbance linear matrix inequality (LMI)

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Improved results on synchronization in arrays of coupled delayed neural networks with hybrid coupling

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Journal of Southeast University(English Edition) 2010年第3期26卷 448-452页

作者：张海涛王婷费树岷李涛东南大学自动化学院南京210096 东南大学复杂工程系统测量与控制教育部重点实验室南京210096

In order to investigate the influence of hybrid coupling on the synchronization of delayed neural networks, by choosing an improved delay-dependent Lyapunov-Krasovskii functional, one less conservative asymptotical criterion based on linear matrix inequality （LMI） is established. The Kronecker product and convex combination techniques are employed. Also the bounds of time-varying delays and delay derivatives are fully considered. By adjusting the inner coupling matrix parameters and using the Matlab LMI toolbox, the design and applications of addressed coupled networks can be realized. Finally, the efficiency and applicability of the proposed results are illustrated by a numerical example with simulations.

关键词： delayed neural networks hybrid coupling synchronization Lyapunov-Krasovskii functional linear matrix inequality (LMI)

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A New Global Asymptotic Stability Result for delayed Cellular neural networks

A New Global Asymptotic Stability Result for Delayed Cellula...

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2009中国控制与决策会议

作者： Jing Liu,Ce Zhang Department of Mathematics and Information science,Binzhou University,Shandong 256603,China

＜正＞This paper studies the problem of global asymptotic stability for delayed cellular neural networks（DCNNs）. A new stability condition is obtained by utilizing the Lyapunov functional method and the matrix inequality approach. This condition is less restrictive and generalizes some of the previous stability results derived in the literature.

关键词： delayed neural networks Global asymptotic stability Lyapunov functionals Matrix inequality

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Exponential convergence and stability of delayed fuzzy cellular neural networks with time-varying coefficients

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控制理论与应用（英文版） 2011年第4期9卷 500-504页

作者： Manchun TAN Department of Mathematics Jinan University Guangzhou Guangdong 510632 China

In this paper, the dynamic behaviors of fuzzy cellular neural networks （FCNNs） with time-varying coefficients and delays are considered. Some criteria are established to ensure the exponential convergence or exponen... 详细信息

关键词： delayed neural networks Exponential convergence Exponential stability Fuzzy cellular neural networks Time-varying coefficients

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Synchronization Control of Discontinuous neural networks via Approximation

Synchronization Control of Discontinuous Neural Networks via...

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2010 Chinese Control and Decision Conference

作者： Xiaoyang Liu,Jinde Cao Department of Mathematics,Southeast University,Nanjing 210096

＜正＞In this paper,complete synchronization is considered for the delayed neural networks with discontinuous activation *** the framework of Filippov solution and in the sense of generalized derivative,a novel control method is given by using approximation and linear matrix inequality（LMI） *** on the detailed analysis of previous works,several criteria are derived to ensure the global asymptotical stability of the error system,and thus the response system synchronizes with the drive *** results are given to illustrate the theoretical results.

关键词： Synchronization delayed neural networks Discontinuous activation functions Filippov solution Generalized derivative LMI approach

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Robust Stability of Markovian Jump neural networks with Mixed Delays

Robust Stability of Markovian Jump Neural Networks with Mixe...

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第二十七届中国控制会议

作者： Sheng Li,Yang Huizhong School of Communication and Control Engineering,Jiangnan University,Wuxi 214122,P.R.China

In this paper,the problem of robust stability for a class of neural networks with Markovian jump parameters and mixed time-delays is *** jump parameters are modeled as a continuous-time,discrete-state Markov process and the mixed delays comprise discrete and distributed *** on the Lyapunov stability theory and linear matrix inequality（LMI） technique,some robust stability conditions for the Markovian jump neural networks with mixed delays are *** proposed LMI-based criteria are computationally efficient and they can be solved readily with recently developed numerical *** example is given to show the effectiveness of the obtained results.

关键词： delayed neural networks Robust stability Markovian jump Mixed time-delays Linear matrix inequality

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Delay neural Network Security Event Triggered Filtering Under Dos Attack

Delay Neural Network Security Event Triggered Filtering Unde...

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第43届中国控制会议

作者： Yaru Feng Hongqian Lu School of Information and Automation QiLu University Of Technology

neural networks(NNs) are a type of artificial network system comprised of numerous interconnected basic processing units. In recent years, neural networks have been widely applied in computer technology, bioinformatics, image recognition,automation, and other fields, becoming an area of active research. In the relevant studies of neural networks, the filtering problem holds significant theoretical significance and practical value. This paper investigates the event-triggered filtering problem of delayed neural networks under Denial-of-Service(DoS) attacks. Firstly, a denial-of-service attack model is established;secondly,deep neural networks, event-triggering mechanisms, and denial-of-service attacks are integrated integrated into a cohesive framework, defining a switching filtering system and establishing a new filtering error model;then, using Lyapunov stability theory and Linear Matrix Inequality(LMI) techniques, the sufficient conditions for exponential mean-square stability of this mathematical model are derived. Reasonable boundary techniques are selected to handle delay-related terms in the Lyapunov-Krasowski functional derivative. Additionally, a new sufficient condition for the coordinated design of the filter and event-triggering parameters is derived in LMI form. At last, the proposed method's effectiveness has been validated through numerical examples.

关键词： delayed neural networks Denial-of-Service Attack Event triggering mechanism Linear matrix inequality filter

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