Extended dissipative state estimation for memristive neural networks with time-varying delay

Xiao, Jianying; Li, Yongtao; Zhong, Shouming; Xu, Fang

doi:10.1016/j.isatra.2016.05.007

Cited by 64 publications

(12 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Complex networks have been extensively studied over the past few decades because complex dynamical networks (CDNs) have been widely applied to many fields, such as optimization problem, biology, signal processing, and industrial automation 1-5 . For these applications, complex networks strongly depend on the dynamic behavior of the network, such as stability, 6 dissipativity, 7 and synchronization 8,9 .…”

Section: Introductionmentioning

confidence: 99%

Novel results on nonfragile sampled‐data exponential synchronization for delayed complex dynamical networks

Zhao

Zhong

Zhang

et al. 2020

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

This article investigates exponential synchronization for complex dynamical networks (CDNs) with nonfragile sampled-data feedback control. An important yet challenging problem that contains the time delays of the controller, the uncertainties occurrence and randomness of controller gain fluctuation due to the packet loss, and time delays during data transmission is to be solved. A novel Lyapunov-Krasovskii functional (LKF) that allows for more free matrix terms and slacks certain one of the terms to nonpositive definite is first constructed based upon special refined block matrices. By employing the novel LKF and functional analysis theory, the nonfragile sampled-data feedback controller is developed to guarantee the exponential synchronization of CDNs and make a more optimal bound estimation of the sampled period. Finally, compared simulation examples are performed as significant to demonstrate the effective performance and the superiority of the proposed methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Novel results on nonfragile sampled‐data exponential synchronization for delayed complex dynamical networks

Zhao

Zhong

Zhang

et al. 2020

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

show abstract

“…If not properly taken into account, the time‐delays would cause the undesirable dynamical behaviors, such as instability, oscillation and other poor performance. In the past several years, the time‐delay phenomenon has attracted considerable research attention from the scholars in the dynamic analysis problems for various NNs, such as Hopfield NNs , recurrent NNs , cellular NNs , memristor NNs , impulsive NNs , stochastic NNs , Markov jump NNs . In the previous research of GNNs, the concerned delay was often constant h > 0 or time‐varying h ( t ), which belonged to an interval 0 < h ( t ) ≤ h 2 .…”

Section: Introductionmentioning

confidence: 99%

“… investigated an issue of extended dissipativity state estimation for generalized neural networks with mixed time‐varying delay signals via newly proposed double integral inequality. Therefore, recently a great number of significant results have been suggested in the extended dissipativity analysis of NNs with time delays .…”

Section: Introductionmentioning

confidence: 99%

Non‐Fragile Extended Dissipativity Control Design for Generalized Neural Networks with Interval Time‐Delay Signals

Manivannan

Samidurai

Cao

et al. 2018

Asian Journal of Control

View full text Add to dashboard Cite

The problem of non-fragile extended dissipative control design for a class of generalized neural networks (GNNs) with interval time-delay signals is investigated in this paper. By constructing a suitable Lyapunov-Krasovskii functional (LKF) with double and triple integral terms, and estimating their derivative by using the Wirtinger single integral inequality (WSII) and Wirtinger double integral inequality (WDII) technique respectively, and that is mixed with the reciprocally convex combination (RCC) approach. A new delay-dependent non-fragile extended dissipative control design for GNNs are expressed in terms of the linear matrix inequalities (LMIs). Then, the desired non-fragile extended dissipative controller can be obtained by solving the linear matrix inequalities (LMIs). Furthermore, a non-fragile state feedback controller is designed for GNNs such that the closed-loop system is extended dissiptive. Thus, the non-fragile extended dissipative controller can be adopted to deal with the non-fragile  2 − ∞ performance, non-fragile  ∞ performance, non-fragile passive performance, non-fragile mixed  ∞ and passivity performance, and non-fragile dissipative performance for GNNs by selecting the weighting matrices. Finally, simulation studies are demonstrated for showing the feasibility of the proposed method. Among them, one example was supported by the real-life application of the quadruple tank process system.

show abstract

“…Therefore, since 2008, its potential applications have become more and more popular in many aspects such as generation computer, powerful brain-like neural computer, and so on. There is no doubt that it has initiated the worldwide concern with the emergence of the memristor (see [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]). For the neural networks, the first job is considering whether they are stable or not.…”

Section: Introductionmentioning

confidence: 99%

Design disturbance attenuating controller for memristive recurrent neural networks with mixed time-varying delays

Xiao

Zhong

2018

Adv Differ Equ

Self Cite

View full text Add to dashboard Cite

This paper investigates the design of disturbance attenuating controller for memristive recurrent neural networks (MRNNs) with mixed time-varying delays. By applying the combination of differential inclusions, set-valued maps and Lyapunov-Razumikhin, a feedback control law is obtained in the simple form of linear matrix inequality (LMI) to ensure disturbance attenuation of memristor-based neural networks. Finally, a numerical example is given to show the effectiveness of the proposed criteria.

show abstract

Extended dissipative state estimation for memristive neural networks with time-varying delay

Cited by 64 publications

References 40 publications

Novel results on nonfragile sampled‐data exponential synchronization for delayed complex dynamical networks

Novel results on nonfragile sampled‐data exponential synchronization for delayed complex dynamical networks

Non‐Fragile Extended Dissipativity Control Design for Generalized Neural Networks with Interval Time‐Delay Signals

Design disturbance attenuating controller for memristive recurrent neural networks with mixed time-varying delays

Contact Info

Product

Resources

About