Passivity analysis of memristive neural networks with different memductance functions

Memristive neural networks have captured the attention of physicists, biologists, ecologists, economists and social scientists. In this paper, we formulate and investigate a class of memristive neural networks with two different types of memductance functions. Some succinct criteria in terms of linear matrix inequalities for the passivity are proposed. Meanwhile, based on the derived criteria, some stability criterion are obtained for the memristive neural networks. These theoretical analysis can characterize the fundamental electrical properties of memristive systems and provide convenience for applications.     

Lagrange exponential stability of quaternion-valued memristive neural networks with time delays

This paper investigates the Lagrange global exponential stability of the quaternion-valued memristive neural networks (QVMNNs). Two kinds of activation functions based on different assumptions are considered. Then, based on the Lyapunov function approach, decomposition method, and some inequality skills, two novel sufficient conditions for lagrange stability of QVMNNs are provided corresponding to different types of activation functions. Lastly, simulation examples are provided to demonstrate the correctness of our theoretical results.     

Mittag-Leffler stability and adaptive impulsive synchronization of fractional order neural networks in quaternion field

Fractional order quaternion-valued neural networks are a type of fractional order neural networks for which neuron state, synaptic connection strengths, and neuron activation functions are quaternion. This paper is dealing with the Mittag-Leffler stability and adaptive impulsive synchronization of fractional order neural networks in quaternion field. The fractional order quaternion-valued neural networks are separated into four real-valued systems forming an equivalent four real-valued fractional order neural networks, which decreases the computational complexity by avoiding the noncommutativity of quaternion multiplication. Via some fractional inequality techniques and suitable Lyapunov functional, a brand new criterion is proposed first to ensure the Mittag-Leffler stability for the addressed neural networks. Besides, the combination of quaternion-valued adaptive and impulsive control is intended to realize the asymptotically synchronization between two fractional order quaternion-valued neural networks. Ultimately, two numerical simulations are provided to check the accuracy and validity of our obtained theoretical results.     

Anti-synchronization control of a class of memristive recurrent neural networks

In this paper, we formulate and investigate a class of memristive recurrent neural networks. Two different types of anti-synchronization algorithms are derived to achieve the exponential anti-synchronization of the coupled systems based on drive–response concept, differential inclusions theory and Lyapunov functional method. The proposed anti-synchronization algorithms are simple and can be easily realized. The analysis in the paper employs results from the theory of differential equations with discontinuous right-hand side as introduced by Filippov. The obtained results extend some previous works on conventional recurrent neural networks.     

Finite‐time synchronization of memristive neural networks with time‐varying delays via two control methods

In this paper, on the basis of the Lyapunov stability theory and finite‐time stability lemma, the finite‐time synchronization problem for memristive neural networks with time‐varying delays is studied by two control methods. First, the discontinuous state‐feedback control rule containing integral part for square sum of the synchronization error and the discontinuous adaptive control rule are designed for realizing synchronization of drive‐response memristive neural networks in finite time, respectively. Then, by using some important inequalities and defining suitable Lyapunov functions, some algebraic sufficient criteria guaranteeing finite‐time synchronization are deduced for drive‐response memristive neural networks in finite time. Furthermore, we give the estimation of the upper bounds of the settling time of finite‐time synchronization. Lastly, the effectiveness of the obtained sufficient criteria guaranteeing finite‐time synchronization is validated by simulation.     

Functional differential inclusions and dynamic behaviors for memristor-based BAM neural networks with time-varying delays

In this paper, we formulate and investigate a class of memristor-based BAM neural networks with time-varying delays. Under the framework of Filippov solutions, the viability and dissipativity of solutions for functional differential inclusions and memristive BAM neural networks can be guaranteed by the matrix measure approach and generalized Halanay inequalities. Then, a new method involving the application of set-valued version of Krasnoselskii’ fixed point theorem in a cone is successfully employed to derive the existence of the positive periodic solution. The dynamic analysis in this paper utilizes the theory of set-valued maps and functional differential equations with discontinuous right-hand sides of Filippov type. The obtained results extend and improve some previous works on conventional BAM neural networks. Finally, numerical examples are given to demonstrate the theoretical results via computer simulations.     

Multistability of memristive neural networks with time‐varying delays

The recent discovery of memristive neurodynamic systems holds great promise for realizing large‐scale nanoionic circuits. Development of pattern memory analysis for memristive neurodynamic systems poses several challenges. In this article, it shows that an n‐dimensional memristive neural networks with time‐varying delays can have 2ⁿ locally exponentially stable equilibria in the saturation region. In addition, local exponential stability of delayed memristive neural networks in any designated region is also characterized, which allows the locally exponentially stable equilibria to locate in the designated region. All of these criteria are very easy to be verified. Finally, the effectiveness of the results are illustrated by two numerical examples. © 2014 Wiley Periodicals, Inc. Complexity 21: 177–186, 2015     

Existence and global exponential stability of almost periodic solution for quaternion-valued high-order Hopfield neural networks with delays via a direct method

This paper deals with the existence and global exponential stability of almost periodic solutions for quaternion-valued high-order Hopfield neural networks with delays by a direct approach. Based on the contraction mapping principle, sufficient conditions are derived to ensure the existence and uniqueness of almost periodic solutions for the networks under consideration. By constructing a suitable Lyapunov function, the global exponential stability criterion of the almost periodic solution are derived. Finally, two numerical examples are given to illustrate the main results of this paper.     

Synchronization of quaternion-valued coupled systems with time-varying coupling via event-triggered impulsive control

In this paper, the problem of exponential synchronization of quaternion-valued coupled systems based on event-triggered impulsive control is investigated for the first time. It should be pointed out that the coupling strength is quaternion-valued and time-varying, which makes our model more in line with practical models. First, we prove that event-triggered impulsive control can exclude Zeno behavior. Then, based on the Lyapunov method and the graph theory, some sufficient conditions are derived to ensure that quaternion-valued coupled systems reach synchronization. Furthermore, as an application of our theoretical results, exponential synchronization of quaternion-valued Kuramoto oscillators is studied in detail and a synchronization criterion is presented. Finally, some numerical simulations are given to show the effectiveness of our theoretical results.     

Quasi-projective synchronization analysis for delayed stochastic quaternion-valued neural networks via state-feedback control strategy

In this paper, we explore the complete synchronization and quasi-projective synchronization in a class of stochastic delayed quaternion-valued neural networks, utilizing a state-feedback control scheme. The studied neural networks into real-valued networks are short of known decomposing, by designing a very general nonlinear controller, according to the quaternion form It\^{o} formula with a number of inequality techniques in the configuration of quaternion domain, we obtained a quasi-projective synchronization criterion for drive-response networks. Moreover, we estimate the error margin for quasi-projective synchronization. At last, the theoretical results are confirmed by a numerical simulation.     

Dissipativity and synchronization control of fractional‐order memristive neural networks with reaction‐diffusion terms

This paper deals with the dissipativity and synchronization control of fractional‐order memristive neural networks (FOMNNs) with reaction‐diffusion terms. By means of fractional Halanay inequality, Wirtinger inequality, and Lyapunov functional, some sufficient conditions are provided to ensure global dissipativity and exponential synchronization of FOMNNs with reaction‐diffusion terms, respectively. The underlying model and the obtained results are more general since the reaction‐diffusion terms are first introduced into FOMNNs. The given conditions are easy to be checked, and the correctness of the obtained results is confirmed by a living example.     

Exponential synchronization of memristive Hindmarsh–Rose neural networks

A new model of neural networks in terms of the memristive Hindmarsh–Rose equations is proposed. Globally dissipative dynamics is shown with absorbing sets in the state spaces. Through sharp and uniform grouping estimates and by leverage of integral and interpolation inequalities tackling the linear network coupling against the memristive nonlinearity, it is proved that exponential synchronization at a uniform convergence rate occurs when the coupling strengths satisfy the threshold conditions which are quantitatively expressed by the parameters.     

一类四元数小波包的构造

实值二维信号可以用四元数来表示,因此,四元数的尺度函数和小波的构造就成为分析二维信号的关键．引入了四元数小波包的概念,并且借助于四元数多分辨分析和四元数尺度函数和四元数小波函数的概念和若干公式,给出并构造了一类四元数正交小波包的构造方法,得到了四元数正交小波包的3个正交性公式,最后,利用四元数正交小波包给出了L^2（R...     

Global asymptotic stability analysis for integro-differential systems modeling neural networks with delays

In this paper, the global asymptotic stability of the equilibrium point of integro-differential systems modeling neural networks with time-varying delays are studied. Proper Lyapunov functionals and some analytic techniques are employed to derive the sufficient conditions under which the networks proposed are the global asymptotic stability. The results have shown to improve the previous global asymptotic stability results derived in the literature. Some examples are given to illustrate the correctness of our results.     

Reliable stability and stabilizability for complex-valued memristive neural networks with actuator failures and aperiodic event-triggered sampled-data control

This study addresses the stability and stabilizability problems for complex-valued memristive neural networks (CVMNNs) with actuator failures via reliable aperiodic event-triggered sampled-data control. Different from the traditional control methods with time-triggered mechanism, an aperiodic event-triggered sampled-data control scheme is first proposed for CVMNNs. Taking the influence of actuator failures into account, a reliable controller is designed. In comparison with the existing control approaches, the one here is not only more applicable but effective to save the communication resources for CVMNNs. Then, a new Lyapunov–Krasovskii functional (LKF) is introduced, which can fully capture the information of sampling and complex-valued activation functions. Based on the LKF and some new estimation techniques, novel stability and stabilizability criteria are established, and the desired reliable aperiodic event-triggered sampled-data controller gains are obtained simultaneously. Finally, numerical simulations are provided to verify the effectiveness of the obtained theoretical results.     

Global Exponential Stability in Lagrange Sense for Delayed Memristive Neural Networks with Parameter Uncertainties

This paper addresses the Lagrange stability of memristive neural networks with leakage delay and time-varying transmission delays as well as parameter uncertainties. Based on the theory of Filippov''s solution, by using Lyapunov-Krasovskii functionals and the free-weighting matrix method, sufficient conditions in terms of linear matrix inequality (LMI) are given to ascertain the networks with different kinds of activation functions to be stable in Lagrange sense. Meanwhile the estimation of globally attractive sets are given. Finally, numerical simulations are carried out to illustrate the effectiveness of theoretical results.     

Dissipativity analysis of memristive neural networks with time‐varying delays and randomly occurring uncertainties

Dissipativity theory is a very important concept in the field of control system. In this paper, we pay attention to the problem of dissipativity analysis of memristive neural networks with time‐varying delay and randomly occurring uncertainties(ROUs). Under the framework of Filippov solution, differential inclusion theory, by employing a proper Lyapunov functional, and some inequality techniques, the dissipativity criteria are obtained in terms of LMIs. It should be noteworthy that the uncertainty terms as well as the ROUs are separately taken into consideration, in which the uncertainties are norm‐bounded and the ROUs obey certain mutually uncorrelated Bernoulli‐distributed white noise sequences. Finally, the effectiveness of the proposed method will be verified via numerical example. Copyright © 2015 John Wiley & Sons, Ltd.     

Global robust stability for delayed neural networks with polytopic type uncertainties

In this paper, global robust stability for delayed neural networks is studied. First the free-weighting matrices are employed to express the relationship between the terms in the system equation, and a stability condition for delayed neural networks is derived by using the S-procedure. Then this result is extended to establish a global robust stability criterion for delayed neural networks with polytopic type uncertainties. A numerical example given in [IEEE Trans Circuits Syst II 52 (2005) 33–36] for interval delayed neural networks is investigated. The effectiveness of the presented global robust stability criterion and its improvement over the existing results are demonstrated.     

Global robust stability for delayed neural networks with polytopic type uncertainties

In this paper, global robust stability for delayed neural networks is studied. First the free-weighting matrices are employed to express the relationship between the terms in the system equation, and a stability condition for delayed neural networks is derived by using the S-procedure. Then this result is extended to establish a global robust stability criterion for delayed neural networks with polytopic type uncertainties. A numerical example given in [IEEE Trans Circuits Syst II 52 (2005) 33–36] for interval delayed neural networks is investigated. The effectiveness of the presented global robust stability criterion and its improvement over the existing results are demonstrated.