Finite-Time $$L_\infty $$ Performance State Estimation of Recurrent Neural Networks with Sampled-Data Signals

Gunasekaran, Nallappan; Ali, M. Syed; Pavithra, S.

doi:10.1007/s11063-019-10114-9

Cited by 14 publications

(6 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) Developed sufficient conditions can be directly solved by the standard Matlab LMI toolbox. (4) The results of this study are more general and powerful than those in the existing discrete-time QVNN models in the literature.…”

Section: Introductionmentioning

confidence: 78%

“…The research on dynamical behavior analysis for NN models has attracted increasing attention in recent years and their results have been widely used in a variety of science and engineering disciplines [1][2][3][4][5][6][7][8][9]. The stability analysis of NN models is fundamental and important in the applications of NNs, which have received significant attention recently .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discrete-Time Stochastic Quaternion-Valued Neural Networks with Time Delays: An Asymptotic Stability Analysis

et al. 2020

View full text Add to dashboard Cite

Stochastic disturbances often cause undesirable characteristics in real-world system modeling. As a result, investigations on stochastic disturbances in neural network (NN) modeling are important. In this study, stochastic disturbances are considered for the formulation of a new class of NN models; i.e., the discrete-time stochastic quaternion-valued neural networks (DSQVNNs). In addition, the mean-square asymptotic stability issue in DSQVNNs is studied. Firstly, we decompose the original DSQVNN model into four real-valued models using the real-imaginary separation method, in order to avoid difficulties caused by non-commutative quaternion multiplication. Secondly, some new sufficient conditions for the mean-square asymptotic stability criterion with respect to the considered DSQVNN model are obtained via the linear matrix inequality (LMI) approach, based on the Lyapunov functional and stochastic analysis. Finally, examples are presented to ascertain the usefulness of the obtained theoretical results.

show abstract

Section: Introductionmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Discrete-Time Stochastic Quaternion-Valued Neural Networks with Time Delays: An Asymptotic Stability Analysis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In addition, the finite time control procedures have shown better power and unsettling influence dismissal properties. As of late, finite time synchronization of complex systems researched in [29,9,4,1]. In flag transmission, the flag will end up frail because of dispersion in flag transmission, so it is critical to think about that the enactment fluctuates in space just as in time and the response dissemination impacts can't be ignored in both organic and man-made models [31,16].…”

Section: Syed Ali Palanisamy Gunasekaran Alsaedi and Ahmadmentioning

confidence: 99%

Finite-time exponential synchronization of reaction-diffusion delayed complex-dynamical networks

Ali¹,

Palanisamy²,

Gunasekaran³

et al. 2021

Discrete &Amp; Continuous Dynamical Systems - S

View full text Add to dashboard Cite

This investigation looks at the issue of finite time exponential synchronization of complex dynamical systems with reaaction diffusion term. This reort studies complex networks consisting of N straightly and diffusively coupled networks. By building a new Lyapunov krasovskii functional (LKF), using Jensens inequality and convex algorithms approach stability conditions frameworks are determined. At last, a numerical precedent is given to demonstrate the practicality of the theoretical results.Complex dynamical systems (CDNs) have gotten impressive consideration because of their broad applications in useful society, for example, guideline of intensity framework, parallel picture preparing, the activity of no-man air vehicle, the acknowledgment of chain explosion, and so on., (see [12,35,27,2,3,5] and references in that). As of late, complex systems can be seen all over the place and are turning into an essential piece of our day by day life. The absolute most surely understood models incorporate nourishment networks, correspondence systems, interpersonal organizations, control matrices, cell systems, WWW, metabolic networks, illness transmission systems, and so on. Along these lines, the topology and dynamical conduct of complex dynamical systems have been broadly considered by the scientists ([8, 17, 21, 9]). Specifically, the synchronization issue of complex dynamical systems has gotten much concentration as of late ([34, 25]).

show abstract

“…Some advanced control concepts have been well applied in the vibration control system of building structure. Linear matrix inequality (LMI) and Takagi-Sugeno (T-S) fuzzy submodels have been applied to the sampling data control of nonlinear systems [18,19]. Moreover, the stability of the control system has become a research heated issue.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Control Strategies of Adjacent Building Structures Vibration under Earthquake Excitation

Kang

Zhang

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

A decentralized control scheme can effectively solve the control problem of civil engineering structure vibration under earthquake. This paper takes a research into the decentralized control scheme of adjacent buildings when the earthquake happens. It combines overlapping decentralized control method and linear matrix inequality (LMI) with H ∞ control algorithm and puts forward the overlapping decentralized H ∞ control method. A simplified dynamical model of structural vibration control has been established considering the topology structural features of adjacent buildings. The H ∞ control algorithm is applied into each dynamically different subsystems and can be also served as the decentralized H ∞ controllers. Therefore, by contracting decentralized H ∞ controllers to original state space, overlapping decentralized H ∞ controllers are obtained. In this manner, the adjacent buildings’ structure model is analyzed in terms of simulation and calculation which provides a comprehensive insight into vibration control. The results show that the centralized control, the decentralized control, and the overlapping decentralized control, based on linear matrix inequality, can be nearly effective in cases above satisfactorily. Besides, it can also reduce the computational cost as well as increase the flexibility of controller design.

show abstract

Finite-Time $$L_\infty $$ Performance State Estimation of Recurrent Neural Networks with Sampled-Data Signals

Cited by 14 publications

References 36 publications

Discrete-Time Stochastic Quaternion-Valued Neural Networks with Time Delays: An Asymptotic Stability Analysis

Discrete-Time Stochastic Quaternion-Valued Neural Networks with Time Delays: An Asymptotic Stability Analysis

Finite-time exponential synchronization of reaction-diffusion delayed complex-dynamical networks

Decentralized Control Strategies of Adjacent Building Structures Vibration under Earthquake Excitation

Contact Info

Product

Resources

About