New delay-dependent conditions for finite-time extended dissipativity based non-fragile feedback control for neural networks with mixed interval time-varying delays

Zamart, Chantapish; Botmart, Thongchai; Weera, Wajaree; Charoensin, Suphachai

doi:10.1016/j.matcom.2021.07.007

Cited by 7 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hardware implementation of the neural network involves some unavoidable signal transmission delays, which can result in unwanted dynamic behavior like instability, shuttering, oscillation, chaos, and poor performance of NNs; see, for example, and refer the papers [13][14][15] for more details. Thus, when examining the stability of NNs, time delays must be considered, and many relevant reports have been published; for details, one can refer [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Parametric uncertainty is commonly caused by poor modeling, changes in the model's environment, and electronic component tolerances.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust stability of uncertain stochastic switched inertial neural networks with time‐varying delay using state‐dependent switching law

Banu¹,

Banupriya²,

Krishnasamy

et al. 2023

Math Methods in App Sciences

View full text Add to dashboard Cite

In this paper, the problem of robust stability analysis for a type of uncertain stochastic switched inertial neural networks (SSINNs) with time-varying delay is investigated. First, the original second-order system is converted into first-order differential equations using the variable transformation method. Next, some sufficient conditions in terms of linear matrix inequalities (LMIs) are obtained using Lyapunov-Krasovskii functional (LKF), state-dependent switching (SDS) method, and Jensen's integral inequality for estimating integral inequalities so that the augmented system is robust, global, and asymptotically stable in the mean square for the uncertain SSINNs with time-varying delay. It is shown that the stability of the above considered system composed of all unstable subsystems can be achieved by using the SDS law. Finally, two numerical simulations are provided to demonstrate the effectiveness of the proposed SDS law.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Parametric uncertainty is commonly caused by poor modeling, changes in the model's environment, and electronic component tolerances. As a result, time delays and parameter uncertainty can be considered in network models, and the details can be seen in [20][21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Robust stability of uncertain stochastic switched inertial neural networks with time‐varying delay using state‐dependent switching law

Banu¹,

Banupriya²,

Krishnasamy

et al. 2023

Math Methods in App Sciences

View full text Add to dashboard Cite

show abstract

“…On the other hand, these kinds of difficulties provide some precious opportunities to enjoy the use of the modern mathematics to build the tool and to prove the stability based on the obtained tool. In this way, every interested follower can find a great collection of the research works in the literature, and here, we just suggest some samples and cited bibliography therein to more consultation [5–9, 11, 12, 15–19, 21–25, 27–41, 42–56].…”

Section: Introductionmentioning

confidence: 99%

Globally asymptotic stability analysis for memristor‐based competitive systems of reaction–diffusion delayed neural networks

Gholami

2023

Math Methods in App Sciences

View full text Add to dashboard Cite

The main aim of this investigation is to present a mathematical criterion that guarantees the global asymptotic stability of special class of nonlinear neural systems. Indeed, a memristor‐based competitive nonlinear reaction–diffusion Cohen–Grossberg neural network system is chosen to be investigated. Chasing the accuracy and comprehensiveness, our model has to be improved with the following elements: neutrality of the nonlinear part and being effected under time delays in the both of straight and neutral parts of the nonlinearity. Since the proposed neural network is novel, so we need to make use of a novel Lyapunov functional correspondingly to globally asymptotic stabilization of the neural network under investigation. Prior to this process, it is necessary to present a uniqueness criterion for solutions of the proposed neural network. To this aim, the well‐known M‐matrix technique of the linear algebra is applied that guarantees existence of a unique equilibrium point of the considered Cohen–Grossberg neural network. Numerical perspective of this investigation leads us to some numerical prototypes that justify our stability criterion is applicable. This investigation will be finalized with interesting discussion on the nature of time delays that are characterized as fundamental elements in modeling of the neural networks.

show abstract

New Results on Robust Finite-Time Extended Dissipativity for Uncertain Fractional-Order Neural Networks

Tuan

Thành

Thuận

2023

Neural Process Lett

View full text Add to dashboard Cite

New delay-dependent conditions for finite-time extended dissipativity based non-fragile feedback control for neural networks with mixed interval time-varying delays

Cited by 7 publications

References 33 publications

Robust stability of uncertain stochastic switched inertial neural networks with time‐varying delay using state‐dependent switching law

Robust stability of uncertain stochastic switched inertial neural networks with time‐varying delay using state‐dependent switching law

Globally asymptotic stability analysis for memristor‐based competitive systems of reaction–diffusion delayed neural networks

New Results on Robust Finite-Time Extended Dissipativity for Uncertain Fractional-Order Neural Networks

Contact Info

Product

Resources

About