Memristor-based Inertial Neural Networks Research Articles

Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks.

In this brief, we consider the stability of inertial memristor-based neural networks with time-varying delays. First, delayed inertial memristor-based neural networks are modeled as continuous systems in the flux-current-voltage-time domain via the mathematical model of Hewlett-Packard (HP) memristor. Then, they are reduced to delayed inertial neural networks with interval parameters uncertainties. Quasi-equilibrium points and quasi-stability are proposed. Quasi-stability criteria of delayed inertial memristor-based neural networks are obtained by matrix measure method, the Halanay inequality, and uncertainty technologies. In the end, a numerical example is provided to show the validity of our results.

New Results on Finite-Time Synchronization Control of Chaotic Memristor-Based Inertial Neural Networks with Time-Varying Delays

In this work, we are concerned with the finite-time synchronization (FTS) control issue of the drive and response delayed memristor-based inertial neural networks (MINNs). Firstly, a novel finite-time stability lemma is developed, which is different from the existing finite-time stability criteria and extends the previous results. Secondly, by constructing an appropriate Lyapunov function, designing effective delay-dependent feedback controllers and combining the finite-time control theory with a new non-reduced order method (NROD), several novel theoretical criteria to ensure the FTS for the studied MINNs are provided. In addition, the obtained theoretical results are established in a more general framework than the previous works and widen the application scope. Lastly, we illustrate the practicality and validity of the theoretical results via some numerical examples.

Asymptotic and Finite-Time Synchronization of Fractional-Order Memristor-Based Inertial Neural Networks with Time-Varying Delay

This paper emphasized on studying the asymptotic synchronization and finite synchronization of fractional-order memristor-based inertial neural networks with time-varying latency. The fractional-order memristor-based inertial neural network model is offered as a more general and flexible alternative to the integer-order inertial neural network. By utilizing the properties of fractional calculus, two lemmas on asymptotic stability and finite-time stability are provided. Based on the two lemmas and the constructed Lyapunov functionals, some updated and valid criteria have been developed to achieve asymptotic and finite-time synchronization of the addressed systems. Finally, the effectiveness of the proposed method is demonstrated by a number of examples and simulations.

Finite-time stabilization of memristor-based inertial neural networks with time-varying delays combined with interval matrix method

This paper investigates the finite-time stability problem of memristor-based inertial neural networks (MINNs) with time-varying delays in association with the interval matrix approach. In the light of differential inclusion and set-valued mapping, the delayed MINNs are converted to a category of systems with uncertain terms by means of convex combination transformations of matrices to deal with the problem of the parameter mismatch of MINNs. Afterwards, the second-order differential systems are simplified to the ordinary first-order differential systems utilizing dimension reduction. Then, two types of delayed feedback controllers are designed to ensure that MINNs can achieve finite-time stabilization, which handles time-varying delays in different ways. Meanwhile, the finite-time stabilization criterion can be deduced by linear matrix inequalities (LMIs). Therefore, the upper bound on the settling time is estimated as one of the indicators to measure the stability effect. Finally, the accuracy of the theoretical results and the validity of the proposed method are verified by two examples.

Fixed-Time Synchronization of Second-Order MNNs in Quaternion Field

In this paper, we propose a novel class of networks named as quaternion-valued inertial memristor-based neural networks (QVIMNNs) by introducing inertial term and memristor into traditional quaternion-valued neural networks (QVNNs). The problem of fixed-time synchronization of the QVIMNNs is investigated based on the variable transformation and Lyapunov functional method. It is shown that two types of activation functions are considered and a novel criteria guaranteeing fixed-time synchronization for each cases are then achieved by designing different types of controllers. This paper attempts to pave a new way to investigate neural networks classes with numerical simulations support to demonstrate the correctness of the obtained results.

Finite time Synchronization of Inertial Memristive Neural Networks with Time Varying Delay

Finite time synchronization control of inertial memristor-based neural networks with varying delay is considered. In view of drive and response concept, the sufficient conditions to ensure finite time synchronization issue of inertial memristive neural networks is given. Based on Lyapunov finite time asymptotic theory, a kind of feedback controllers is designed for inertial memristorbased neural networks to realize the finite time synchronization. Based on Lyapunov stability theory, close loop error system can be proved finite time and fixed time stable. Finally, illustrative example is given to illustrate the effectiveness of theoretical results.

Stability analysis for quaternion-valued inertial memristor-based neural networks with time delays

Quaternion-valued inertial memristor-based neural networks with time-varying delays are found to be effective in demonstrating complex dynamic behaviors. Considering the stability of the periodic solution for a novel neural network, we construct a class quaternion-valued inertial memristor-based neural network model by combining with the cellular neural networks and the inertial item with time-varying delay. Based on the continuation theorem of Mawhin’s coincidence degree theory, differential inclusion theory, and inequality techniques, some sufficient criteria for the existence of periodic solutions are obtained. Meanwhile, through constructing a novel Lyapunov functional method, several sufficient conditions have been derived to ensure the global exponential stability of periodic solutions. At last, an example is provided to illustrate the obtained theoretical results of the quaternion-valued inertial memristor-based neural networks model.

Global exponential synchronization via nonlinear feedback control for delayed inertial memristor-based quaternion-valued neural networks with impulses

The issue of global exponential synchronization is addressed for delayed impulsive and time-varying delayed inertial memristor-based quaternion-valued neural networks (IMQVNNs). First, the concept of the Filippov solution for IMQVNNs is introduced by applying differential inclusion theory. Then, a nonlinear feedback controller is designed in the skew field of quaternions to ensure the realization of global exponential synchronization for IMQVNNs. According to this controller, the global exponential synchronization criteria are established based on the Lyapunov stability theory. Moreover, a corollary for the real-valued system is given to compare with the existing conclusions. Finally, two examples are presented to show the validity of the results.

Novel Finite-Time Reliable Control Design for Memristor-Based Inertial Neural Networks With Mixed Time-Varying Delays

The issue of finite-time stabilization (FTS) for the memristor-based inertial neural networks (MINNs) with mixed time-varying delays (MTVDs) is researched by virtue of a new analytical method in this brief. First, an appropriate reliable control strategy is proposed for MINNs, which takes the influence of actuator failures into account. Second, by combining Lyapunov functional theory with new analysis techniques, novel theoretical results to guarantee the FTS for the concerned MINNs are acquired, and the desired reliable controller gains are obtained simultaneously. In additions, compared with the previous research works, the FTS results obtained in this paper are established directly from the MINNs themselves without using variable transformation method. In the end, two simulations are exploited to show the correctness and practicability of the acquired theoretical results.

Synchronization Analysis for Stochastic Inertial Memristor-Based Neural Networks with Linear Coupling

This paper concerns the synchronization problem for a class of stochastic memristive neural networks with inertial term, linear coupling, and time-varying delay. Based on the interval parametric uncertainty theory, the stochastic inertial memristor-based neural networks (IMNNs for short) with linear coupling are transformed to a stochastic interval parametric uncertain system. Furthermore, by applying the Lyapunov stability theorem, the stochastic analysis approach, and the Halanay inequality, some sufficient conditions are obtained to realize synchronization in mean square. The established criteria show that stochastic perturbation is designed to ensure that the coupled IMNNs can be synchronized better by changing the state coefficients of stochastic perturbation. Finally, an illustrative example is presented to demonstrate the efficiency of the theoretical results.

Robust dissipativity analysis for delayed memristor-based inertial neural network

The issue of robust dissipativity for delayed memristor-based inertial neural networks is studied in this article. By employing some famous inequalities, the results on global dissipativity and global robust dissipativity of the delayed memristor-based inertial neural networks are obtained via generalized Lyapunov functional method. Moreover, the positive invariant sets and globally attractive sets of the discussed system are also given. Without making the variable transformation, the second order functional differential system is investigated directly, which is different from the previous works. Finally, some examples and their numerical simulations are shown to corroborate the effectiveness of obtained results.

Finite-time stabilization of memristor-based inertial neural networks with discontinuous activations and distributed delays

In this paper, the finite-time stabilization problem for memristor-based inertial neural networks (MINNs) with discontinuous activations (DAs) and distributed delays is investigated. To deal with the discontinuous property of the MINNs, the nonsmooth analysis theory is invoked. Furthermore, to simplify the MINNs with second-order state derivative, an order-reduced method is adopted. Then the second-order MINNs is transformed into a simpler first-order differential system. Moreover, the verifiable algebraic criteria are derived for the finite-time stabilization of MINNs with DAs and distributed delays under the designed control approach. Finally, the effectiveness of the obtained results are illustrated via numerical simulations.

Passivity Analysis for Memristor-Based Inertial Neural Networks With Discrete and Distributed Delays

The existing results of passivity for neural networks mainly concentrated on first-order derivative of the states, whereas it is also significant to study the passivity with high-order derivative. In this paper, a class of memristor-based inertial neural networks (MINNs) with external inputs and outputs are concerned. First, by choosing appropriate variable transformation, the original networks are rewritten as first-order differential equations. Then a criterion of passivity for the MINNs is presented by nonsmooth analysis and linear matrix inequality (LMI) techniques, which could also result in NP-hard problem since it needs at least exponential-time to solve the passivity condition. Meanwhile, based on the obtained passivity criterion, asymptotic stability criterion is accordingly derived for MINNs. In order to avoid the NP-hard problem, we employ matrix-analysis-techniques with the property that the difference-matrix between the given matrix and the proposed matrix is seminegative definite. Then the time complexity for solving the proposed passivity criterion is reduced to constant-time with respect to the number of LMIs. Robust passivity for MINNs is also studied for bounded uncertain parameters. The MINN widens the application ranges for designing neural networks. Finally, relevant simulation examples are given to show the effectiveness of the obtained results.

Exponential Synchronization of Inertial Memristor-Based Neural Networks with Time Delay Using Average Impulsive Interval Approach

This paper deals with the impulsive synchronization problem for a class of inertial memristor-based neural networks (IMNNs) with time delays by applying average impulsive interval approach. By adopting proper variable transformation, the original system can be converted into first-order differential equations. By utilizing Lyapunov theory, theory of differential inclusion, Halanay inequality and average impulsive interval approach, we attain some adequate conditions that make sure the exponential synchronization of IMNNs under the impulsive control technique. Moreover some delay-dependent conditions for delayed impulsive synchronization of the considered system is obtained. Finally, numerical simulations are offered to exhibit the capacity of our theoretical findings.

Passivity and Passification of Fuzzy Memristive Inertial Neural Networks on Time Scales

A class of Takagi–Sugeno (T–S) fuzzy memristor-based inertial neural networks (FMINNs) is studied on time scales. The second-order derivative of the state variable in the network denotes the inertial term. At first, one timescale-type FMINNs is formulated on the basis of T–S fuzzy rules. By a variable transformation, the original network is transformed into first-order differential equations. Then, passivity criteria for the FMINNs are presented based on the characteristic function approach, linear matrix inequality techniques, and the calculus of time scales. Furthermore, two classes of control protocols, i.e., memristor- and fuzzy-related control protocols are designed to solve the passification problem for the considered FMINNs. The optimization problem of the passivity performance is also involved. Finally, simulation examples are given to show the effectiveness and validity of the obtained results, and an application is also given in pseudorandom number generation.

Fixed-time synchronization of inertial memristor-based neural networks with discrete delay

This paper is concerned with the fixed-time synchronization control of inertial memristor-based neural networks with discrete delay. We design four different kinds of feedback controllers, under which the considered inertial memristor-based neural networks can realize fixed-time synchronization perfectly. Moreover, the obtained fixed-time synchronization criteria can be verified by algebraic operations. For any initial synchronization error, the settling time of fixed-time synchronization is bounded by a fixed constant, which can be calculated beforehand based on system parameters and controller parameters. Numerical simulations are given to illustrate the effectiveness of our theoretical results.

Input-to-state stability of impulsive inertial memristive neural networks with time-varying delayed

The property of input-to-state stability (ISS) of inertial memristor-based neural networks with impulsive effects is studied. Firstly, according to the characteristics of memristor and inertial neural networks, the inertial memristor-based neural networks are built. Secondly, based on the impulsive control theory, the average impulsive interval approach, Halanay differential inequality, Lyapunov method and comparison property, some sufficient conditions ensuring ISS of the inertial memristor-based neural networks under impulsive controller are derived. In this paper, we consider two types of impulse, stabilizing impulses and destabilizing impulses. When the inertial memristor-based neural networks are originally not ISS, by choosing a suitable lower bound of the average impulsive interval, the stabilizing impulses can be used to stabilize the inertial memristor-based neural networks. On the contrary, the inertial memristor-based neural networks are originally ISS, by restricting the upper bound of the average impulsive interval, the ISS of inertial memristor-based neural networks with destabilizing impulses can be ensured. Finally, numerical results are presented to illustrate the main results.

Synchronization criteria for inertial memristor-based neural networks with linear coupling

This paper is concerned with the synchronization problem for an array of memristive neural networks with inertial term, linear coupling and time-varying delay. Since parameters in the connection weight matrices are state-dependent, that is to say, the connection weight matrices jump in certain intervals, the mathematical model of the coupled inertial memristive neural networks can be considered as an interval parametric uncertain system. Based on the interval parametric uncertainty theory, two different synchronization criteria for memristor-based neural networks are obtained by applying the p-matrix measure (p=1,2,∞,ω), Halanay inequality and constructing suitable Lyapunov–Krasovskii functionals. Two simulation examples with fully-connected and nearest neighboring topology are presented to demonstrate the efficiency of the obtained theoretical results.

Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses

In this study, we investigate the global exponential stability of inertial memristor-based neural networks with impulses and time-varying delays. We construct inertial memristor-based neural networks based on the characteristics of the inertial neural networks and memristor. Impulses with and without delays are considered when modeling the inertial neural networks simultaneously, which are of great practical significance in the current study. Some sufficient conditions are derived under the framework of the Lyapunov stability method, as well as an extended Halanay differential inequality and a new delay impulsive differential inequality, which depend on impulses with and without delays, in order to guarantee the global exponential stability of the inertial memristor-based neural networks. Finally, two numerical examples are provided to illustrate the efficiency of the proposed methods.