共查询到20条相似文献,搜索用时 15 毫秒
1.
This paper concerns the problem of global exponential synchronization for a class of memristor-based Cohen–Grossberg neural networks with time-varying discrete delays and unbounded distributed delays. The drive-response set is discussed. A novel controller is designed such that the response (slave) system can be controlled to synchronize with the drive (master) system. Through a nonlinear transformation, we get an alternative system from the considered memristor-based Cohen–Grossberg neural networks. By investigating the global exponential synchronization of the alternative system, we obtain the corresponding synchronization criteria of the considered memristor-based Cohen–Grossberg neural networks. Moreover, the conditions established in this paper are easy to be verified and improve the conditions derived in most of existing papers concerning stability and synchronization for memristor-based neural networks. Numerical simulations are given to show the effectiveness of the theoretical results. 相似文献
2.
This paper deals with the problem of function projective synchronization for a class of memristor-based Cohen–Grossberg neural networks with time-varying delays. Based on the theory of differential equations with
discontinuous right-hand side, some novel criteria are obtained to realize the function projective synchronization of addressed networks by combining open loop control and linear feedback control. As some special cases, several control strategies are given to ensure the realization of complete synchronization, anti-synchronization and the stabilization of the considered memristor-based Cohen–Grossberg neural network. Finally, a numerical example and its simulations are provided to demonstrate the effectiveness of the obtained results. 相似文献
3.
This paper explores the problem of synchronization of a class of generalized reaction-diffusion neural networks with mixed time-varying delays. The mixed time-varying delays under consideration comprise of both discrete and distributed delays. Due to the development and merits of digital controllers, sampled-data control is a natural choice to establish synchronization in continuous-time systems. Using a newly introduced integral inequality, less conservative synchronization criteria that assure the global asymptotic synchronization of the considered generalized reaction-diffusion neural network and mixed delays are established in terms of linear matrix inequalities (LMIs). The obtained easy-to-test LMI-based synchronization criteria depends on the delay bounds in addition to the reaction-diffusion terms, which is more practicable. Upon solving these LMIs by using Matlab LMI control toolbox, a desired sampled-data controller gain can be acuqired without any difficulty. Finally, numerical examples are exploited to express the validity of the derived LMI-based synchronization criteria. 相似文献
4.
This paper investigates drive-response synchronization for a class of
neural networks with time-varying discrete and distributed delays (mixed delays) as
well as discontinuous activations. Strict mathematical proof shows the global
existence of Filippov solutions to neural networks with discontinuous activation
functions and the mixed delays. State feedback controller and impulsive controller
are designed respectively to guarantee global exponential synchronization of the
neural networks. By using Lyapunov function and new analysis techniques, several new
synchronization criteria are obtained. Moreover, lower bound on the convergence rate
is explicitly estimated when state feedback controller is utilized. Results of this
paper are new and some existing ones are extended and improved. Finally, numerical
simulations are given to verify the effectiveness of the theoretical results. 相似文献
5.
This paper investigates the finite-time synchronization and fixed-time synchronization problems of inertial memristive neural networks with time-varying delays. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, several sufficient conditions are derived to ensure finite-time synchronization of inertial memristive neural networks. Then, for the purpose of making the setting time independent of initial condition, we consider the fixed-time synchronization. A novel criterion guaranteeing the fixed-time synchronization of inertial memristive neural networks is derived. Finally, three examples are provided to demonstrate the effectiveness of our main results. 相似文献
6.
The robust asymptotic stability analysis for uncertain BAM neural networks with both interval time-varying delays and stochastic disturbances is considered. By using the stochastic analysis approach, employing some free-weighting matrices and introducing an appropriate type of Lyapunov functional which takes into account the ranges for delays, some new stability criteria are established to guarantee the delayed BAM neural networks to be robustly asymptotically stable in the mean square. Unlike the most existing mean square stability conditions for BAM neural networks, the supplementary requirements that the time derivatives of time-varying delays must be smaller than 1 are released and the lower bounds of time varying delays are not restricted to be 0. Furthermore, in the proposed scheme, the stability conditions are delay-range-dependent and rate-dependent/independent. As a result, the new criteria are applicable to both fast and slow time-varying delays. Three numerical examples are given to illustrate the effectiveness of the proposed criteria. 相似文献
7.
In this paper, the synchronization problem for delayed continuous time nonlinear complex neural networks is considered. The
delay dependent state feed back synchronization gain matrix is obtained by considering more general case of time-varying delay.
Using Lyapunov stability theory, the sufficient synchronization criteria are derived in terms of Linear Matrix Inequalities
(LMIs). By decomposing the delay interval into multiple equidistant subintervals, Lyapunov-Krasovskii functionals (LKFs) are
constructed on these intervals. Employing these LKFs, new delay dependent synchronization criteria are proposed in terms of
LMIs for two cases with and without derivative of time-varying delay. Numerical examples are illustrated to show the effectiveness
of the proposed method. 相似文献
8.
In this paper, the synchronization problem for a class of discrete-time complex-valued neural networks with time-varying delays is investigated. Compared with the previous work, the time delay and parameters are assumed to be time-varying. By separating the real part and imaginary part, the discrete-time model of complex-valued neural networks is derived. Moreover, by using the complex-valued Lyapunov-Krasovskii functional method and linear matrix inequality as tools, sufficient conditions of the synchronization stability are obtained. In numerical simulation, examples are presented to show the effectiveness of our method. 相似文献
9.
Abdulaziz Alofi Fengli Ren Abdullah Al-Mazrooei Ahmed Elaiw Jinde Cao 《Cognitive neurodynamics》2015,9(5):549-559
In this paper, a new synchronization problem for the collective dynamics among genetic oscillators with unbounded time-varying delay is investigated. The dynamical system under consideration consists of an array of linearly coupled identical genetic oscillators with each oscillators having unbounded time-delays. A new concept called power-rate synchronization, which is different from both the asymptotical synchronization and the exponential synchronization, is put forward to facilitate handling the unbounded time-varying delays. By using a combination of the Lyapunov functional method, matrix inequality techniques and properties of Kronecker product, we derive several sufficient conditions that ensure the coupled genetic oscillators to be power-rate synchronized. The criteria obtained in this paper are in the form of matrix inequalities. Illustrative example is presented to show the effectiveness of the obtained results. 相似文献
10.
R. Suresh Kumar G. Sugumaran R. Raja Quanxin Zhu U. Karthik Raja 《Cognitive neurodynamics》2016,10(1):85-98
This paper analyzes the global asymptotic stability of a class of neural networks with time delay in the leakage term and time-varying delays under impulsive perturbations. Here the time-varying delays are assumed to be piecewise. In this method, the interval of the variation is divided into two subintervals by its central point. By developing a new Lyapunov–Krasovskii functional and checking its variation in between the two subintervals, respectively, and then we present some sufficient conditions to guarantee the global asymptotic stability of the equilibrium point for the considered neural network. The proposed results which do not require the boundedness, differentiability and monotonicity of the activation functions, can be easily verified via the linear matrix inequality (LMI) control toolbox in MATLAB. Finally, a numerical example and its simulation are given to show the conditions obtained are new and less conservative than some existing ones in the literature. 相似文献
11.
This paper aims to analyze global robust exponential stability in the mean square sense of stochastic discrete-time genetic
regulatory networks with stochastic delays and parameter uncertainties. Comparing to the previous research works, time-varying
delays are assumed to be stochastic whose variation ranges and probability distributions of the time-varying delays are explored.
Based on the stochastic analysis approach and some analysis techniques, several sufficient criteria for the global robust
exponential stability in the mean square sense of the networks are derived. Moreover, two numerical examples are presented
to show the effectiveness of the obtained results. 相似文献
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13.
14.
The global asymptotic stability of impulsive stochastic Cohen–Grossberg neural networks with mixed delays and reaction–diffusion terms is investigated. Under some suitable assumptions and using Lyapunov–Krasovskii functional method, we apply the linear matrix inequality technique to propose some new sufficient conditions for the global asymptotic stability of the addressed model in the stochastic sense. The mixed time delays comprise both the time-varying and continuously distributed delays. The effectiveness of the theoretical result is illustrated by a numerical example. 相似文献
15.
This paper is concerned with the stability analysis for neural networks with interval time-varying delays and parameter uncertainties.
An approach combining the Lyapunov-Krasovskii functional with the differential inequality and linear matrix inequality techniques
is taken to investigate this problem. By constructing a new Lyapunov-Krasovskii functional and introducing some free weighting
matrices, some less conservative delay-derivative-dependent and delay-derivative-independent stability criteria are established
in term of linear matrix inequality. And the new criteria are applicable to both fast and slow time-varying delays. Three
numerical examples show that the proposed criterion are effective and is an improvement over some existing results in the
literature. 相似文献
16.
In this paper, we extensively study the global asymptotic stability problem of complex-valued neural networks with leakage delay and additive time-varying delays. By constructing a suitable Lyapunov–Krasovskii functional and applying newly developed complex valued integral inequalities, sufficient conditions for the global asymptotic stability of proposed neural networks are established in the form of complex-valued linear matrix inequalities. This linear matrix inequalities are efficiently solved by using standard available numerical packages. Finally, three numerical examples are given to demonstrate the effectiveness of the theoretical results. 相似文献
17.
This paper studies two kinds of synchronization between two discrete-time networks with time delays, including inner synchronization
within each network and outer synchronization between two networks. Based on Lyapunov stability theory and linear matrix inequality
(LMI), sufficient conditions for two discrete-time networks to be asymptotic stability are derived in terms of LMI. Finally
numerical examples are given to illustrate the effectiveness of our derived results. The theoretical understanding provides
insights into the dynamics of two or more neural networks with appropriate couplings. 相似文献
18.
Global and robust stability of interval Hopfield neural networks with time-varying delays 总被引:3,自引:0,他引:3
In this paper, we investigate the problem of global and robust stability of a class of interval Hopfield neural networks that have time-varying delays. Some criteria for the global and robust stability of such networks are derived, by means of constructing suitable Lyapunov functionals for the networks. As a by-product, for the conventional Hopfield neural networks with time-varying delays, we also obtain some new criteria for their global and asymptotic stability. 相似文献
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20.
In this paper, input-to-state stability problems for a class of recurrent neural networks model with multiple time-varying delays are concerned with. By utilizing the Lyapunov–Krasovskii functional method and linear matrix inequalities techniques, some sufficient conditions ensuring the exponential input-to-state stability of delayed network systems are firstly obtained. Two numerical examples and its simulations are given to illustrate the efficiency of the derived results. 相似文献