Mittag-Leffler Synchronization Research Articles

Estimations of global Mittag–Leffler bounds and synchronization for fractional-order permanent magnet synchronous motor systems

This article investigates the global Mittag-Leffler bounds (GM-LBs) and synchronization of fractional-order permanent magnet synchronous motors (FOPMSMs). To begin with, by using some appropriate generalized Lyapunov functions, the extremum principle of function and fractional differential inequalities, a few new bounds for the three-dimensional cylindrical and ellipsoidal domains and a rotatory parabolic body of FOPMSMs are estimated by utilizing the parameters in the systems, which improves the earlier studies and may conclude some new estimates. Moreover, linear feedback control strategies owning one or two inputs and fractional adaptive control owning both single input and single state are accepted to come true the global Mittag-Leffler synchronization (GM-LS) and global asymptotic synchronization (GAS) of two FOPMSMs. Some new criteria for the synchronization are acquired by utilizing some inequality approaches. Finally, the feasibility of the proposed control schemes is verified by numerical simulations.

Adaptive Strategies and its Application in the Mittag-Leffler Synchronization of Delayed Fractional-Order Complex-Valued Reaction-Diffusion Neural Networks

Adaptive Strategies and its Application in the Mittag-Leffler Synchronization of Delayed Fractional-Order Complex-Valued Reaction-Diffusion Neural Networks

Mittag-Leffler Synchronization in Finite Time for Uncertain Fractional-Order Multi-Delayed Memristive Neural Networks with Time-Varying Perturbations via Information Feedback

Mittag-Leffler Synchronization in Finite Time for Uncertain Fractional-Order Multi-Delayed Memristive Neural Networks with Time-Varying Perturbations via Information Feedback

The global Mittag-Leffler synchronization problem of Caputo fractional-order inertial memristive neural networks with time-varying delays

The global Mittag-Leffler synchronization problem of Caputo fractional-order inertial memristive neural networks with time-varying delays

Brain Connectivity Dynamics and Mittag–Leffler Synchronization in Asymmetric Complex Networks for a Class of Coupled Nonlinear Fractional-Order Memristive Neural Network System with Coupling Boundary Conditions

This paper investigates the long-time behavior of fractional-order complex memristive neural networks in order to analyze the synchronization of both anatomical and functional brain networks, for predicting therapy response, and ensuring safe diagnostic and treatments of neurological disorder (such as epilepsy, Alzheimer’s disease, or Parkinson’s disease). A new mathematical brain connectivity model, taking into account the memory characteristics of neurons and their past history, the heterogeneity of brain tissue, and the local anisotropy of cell diffusion, is proposed. This developed model, which depends on topology, interactions, and local dynamics, is a set of coupled nonlinear Caputo fractional reaction–diffusion equations, in the shape of a fractional-order ODE coupled with a set of time fractional-order PDEs, interacting via an asymmetric complex network. In order to introduce into the model the connection structure between neurons (or brain regions), the graph theory, in which the discrete Laplacian matrix of the communication graph plays a fundamental role, is considered. The existence of an absorbing set in state spaces for system is discussed, and then the dissipative dynamics result, with absorbing sets, is proved. Finally, some Mittag–Leffler synchronization results are established for this complex memristive neural network under certain threshold values of coupling forces, memristive weight coefficients, and diffusion coefficients.

Finite-time Mittag-Leffler synchronization of delayed fractional-order discrete-time complex-valued genetic regulatory networks: Decomposition and direct approaches

The complex-valued molecular model of mRNA and protein plays a crucial role in the regulatory mechanisms governing gene expression in the genetic regulatory network (GRN). In this study, we introduced a novel GRN utilizing the fractional-order discrete-time complex-valued molecular model of mRNA and protein. Both decomposition and a direct approach are employed to investigate the nonlinear regulatory function within complex-valued molecular models. New conditions are established to ensure the finite-time Mittag-Leffler synchronization (FTMLS) of the error model dynamics. Feedback controllers are applied to derive the FTMLS conditions for delayed fractional-order discrete-time complex-valued genetic regulatory networks (DFDTCVGRNs). By applying the first-order backward difference on the Lyapunov functional under the definition of fractional Caputo difference and fractional calculus theory, sufficient conditions are derived. Finally, two numerical examples are provided to illustrate the FTMLS criteria obtained.

Function projective Mittag-Leffler synchronization of non-identical fractional-order neural networks

This article investigates the function projective Mittag-Leffler synchronization (FPMLS) between non-identical fractional-order neural networks (FONNs). The stability analysis is carried out using an existing lemma for the Lyapunov function in the FONN systems. Based on the stability theorem of FONN, a non-linear controller is designed to achieve FPMLS. Moreover, global Mittag-Leffler synchronization (GMLS) is investigated in the context of other synchronization techniques, such as projective synchronization (PS), anti-synchronization (AS) and complete synchonization (CS). Using the definition of the Caputo derivative, the Mittag-Leffler function and the Lyapunov stability theory, some stability results for the FPMLS scheme for FONN are discussed. Finally, the proposed technique is applied to a numerical example to validate its efficiency and the unwavering quality of the several applied synchronization conditions.

Asymptotic and Mittag–Leffler Synchronization of Fractional-Order Octonion-Valued Neural Networks with Neutral-Type and Mixed Delays

Very recently, a different generalization of real-valued neural networks (RVNNs) to multidimensional domains beside the complex-valued neural networks (CVNNs), quaternion-valued neural networks (QVNNs), and Clifford-valued neural networks (ClVNNs) has appeared, namely octonion-valued neural networks (OVNNs), which are not a subset of ClVNNs. They are defined on the octonion algebra, which is an 8D algebra over the reals, and is also the only other normed division algebra that can be defined over the reals beside the complex and quaternion algebras. On the other hand, fractional-order neural networks (FONNs) have also been very intensively researched in the recent past. Thus, the present work combines FONNs and OVNNs and puts forward a fractional-order octonion-valued neural network (FOOVNN) with neutral-type, time-varying, and distributed delays, a very general model not yet discussed in the literature, to our awareness. Sufficient criteria expressed as linear matrix inequalities (LMIs) and algebraic inequalities are deduced, which ensure the asymptotic and Mittag–Leffler synchronization properties of the proposed model by decomposing the OVNN system of equations into a real-valued one, in order to avoid the non-associativity problem of the octonion algebra. To accomplish synchronization, we use two different state feedback controllers, two different types of Lyapunov-like functionals in conjunction with two Halanay-type lemmas for FONNs, the free-weighting matrix method, a classical lemma, and Young’s inequality. The four theorems presented in the paper are each illustrated by a numerical example.

Synchronizations control of fractional-order multidimension-valued memristive neural networks with delays

In this paper, systems of the fractional-order multidimension-valued memristive neural networks (FMVMNNs) with leakage delay and time-varying delays are studied in continuous-time and discrete-time cases. Comparative studies on synchronizations control between discrete-time FMVMNNs and the continuous-time system are investigated directly rather than through the decomposition method. By employing Lyapunov theory and fractional derivative inequalities, sufficient criteria about the quasi-synchronization and global ultimate Mittag-Leffler lag quasi-synchronization are obtained in multidimension-valued linear matrix inequality form, while succinct conclusions of asymptotical synchronization and global Mittag-Leffler synchronization are derived in vector form. The obtained criteria have many advantages in a less computation, lower conservatism, more generality and higher flexibility. Finally, four examples are given to demonstrate the effectiveness and availability of the derived conclusions.

New results of global Mittag-Leffler synchronization on Caputo fuzzy delayed inertial neural networks

This article is devoted to discussing the problem of global Mittag-Leffler synchronization (GMLS) for the Caputo-type fractional-order fuzzy delayed inertial neural networks (FOFINNs). First of all, both inertial and fuzzy terms are taken into account in the system. For the sake of reducing the influence caused by the inertia term, the order reduction is achieved by the measure of variable substitution. The introduction of fuzzy terms can evade fuzziness or uncertainty as much as possible. Subsequently, a nonlinear delayed controller is designed to achieve GMLS. Utilizing the inequality techniques, Lyapunov’s direct method for functions and Razumikhin theorem, the criteria for interpreting the GMLS of FOFINNs are established. Particularly, two inferences are presented in two special cases. Additionally, the availability of the acquired results are further confirmed by simulations.

Synchronization of fractional-order reaction-diffusion neural networks via mixed boundary control

This article studies the synchronization issue of fractional reaction-diffusion neural networks (FRDNNs) with time delay and mixed boundary condition. First, a novel boundary controller with constant-valued gain is designed, which only relies on the boundary state information. Subsequently, by virtue of Lyapunov direct technique and LMI approach, the Mittag–Leffler synchronization conditions are established. Besides, to effectively regulate the control gain, a fractional-order adaptive boundary controller is developed and the adaptive synchronization of FRDNNs is rigorously analyzed. Note that, the above control strategies are also workable for traditional integer-order reaction-diffusion neural networks. The developed theoretical analysis is supported eventually via a numerical example.

Synchronization analysis of coupled fractional-order neural networks with time-varying delays.

In this paper, the complete synchronization and Mittag-Leffler synchronization problems of a kind of coupled fractional-order neural networks with time-varying delays are introduced and studied. First, the sufficient conditions for a controlled system to reach complete synchronization are established by using the Kronecker product technique and Lyapunov direct method under pinning control. Here the pinning controller only needs to control part of the nodes, which can save more resources. To make the system achieve complete synchronization, only the error system is stable. Next, a new adaptive feedback controller is designed, which combines the Razumikhin-type method and Mittag-Leffler stability theory to make the controlled system realize Mittag-Leffler synchronization. The controller has time delays, and the calculation can be simplified by constructing an appropriate auxiliary function. Finally, two numerical examples are given. The simulation process shows that the conditions of the main theorems are not difficult to obtain, and the simulation results confirm the feasibility of the theorems.

New criteria on global Mittag-Leffler synchronization for Caputo-type delayed Cohen-Grossberg Inertial Neural Networks

<abstract><p>Our focus of this paper is on global Mittag-Leffler synchronization (GMLS) of the Caputo-type Inertial Cohen-Grossberg Neural Networks (ICGNNs) with discrete and distributed delays. This model takes into account the inertial term as well as the two types of delays, which greatly reduces the conservatism with respect to the model. A change of variables transforms the $ 2\beta $ order inertial frame into $ \beta $ order ordinary frame in order to deal with the effect of the inertial term. In the following steps, two novel types of delay controllers are designed for the purpose of reaching the GMLS. In conjunction with the novel controllers, utilizing differential mean-value theorem and inequality techniques, several criteria are derived to determine the GMLS of ICGNNs within the framework of Caputo-type derivative and calculus properties. At length, the feasibility of the results is further demonstrated by two simulation examples.</p></abstract>

Global Mittag-Leffler synchronization of coupled delayed fractional reaction-diffusion Cohen-Grossberg neural networks via sliding mode control.

This paper studies the sliding mode control method for coupled delayed fractional reaction-diffusion Cohen-Grossberg neural networks on a directed non-strongly connected topology. A novel fractional integral sliding mode surface and the corresponding control law are designed to realize global Mittag-Leffler synchronization. The sufficient conditions for synchronization and reachability of the sliding mode surface are derived via the hierarchical method and the Lyapunov method. Finally, simulations are provided to verify our theoretical findings.

Synchronization analysis of fractional-order inertial-type neural networks with time delays

This paper is dedicated to the global Mittag-Leffler synchronization (GMLS) of fractional-order inertial-type neural networks (FOITNNs) with time delays. To begin with, based on the semigroup property of the Caputo fractional derivative (CFD), an appropriate variable substitution is chosen to transform the original fractional-order inertial system into a traditional fractional-order system. Secondly, two types of discontinuous control schemes with delays and only a control input are proposed: one is the state feedback control and the other is the fractional-order adaptive control. On the basis of Lyapunov stability theory and fractional-order differential inequalities, some new sufficient criteria for the GMLS of two FOITNNs are established. Furthermore, the control gains here can be selected more widely, which makes the results more applicative and less conservative. Finally, two numerical examples validate the efficacy of the obtained results.

Global Mittag–Leffler stability and synchronization of discrete-time fractional-order delayed quaternion-valued neural networks

This paper is devoted to investigating discrete-time fractional-order delayed quaternion-valued neural networks (DFDQNNs) by utilizing direct quaternion approach. Firstly, a novel lemma and its two corresponding corollaries have been proposed for estimating nabla fractional difference of the quaternion-valued Lyapunov function. Then, the existence and uniqueness of equilibrium point for DFDQNNs is proved by constructing a new quaternion-valued contraction mapping. In addition, by means of our designed Lyapunov functions and the effective feedback controller as well as neoteric nabla difference inequalities, some sufficient criteria have been obtained to ensure the global Mittag–Leffler stability and Mittag–Leffler synchronization of DFDQNNs, respectively. Finally, some numerical examples are provided to verify the yielded results.

Global synchronization for BAM delayed reaction-diffusion neural networks with fractional partial differential operator

This paper is devoted to the exploration of the global asymptotic synchronization (GAS) and global Mittag–Leffler synchronization (GMLS) issues on bidirectional associative memory reaction-diffusion neural networks (BAMRDNNs), which include a Caputo’s fractional partial differential operator, leakage and discrete delays. By means of the Green’s theorem, Jensen’s integral inequality and Lyapunov functional approach, several synchronization criteria are established by using the hybrid controllers. The presented results are described in the algebraic inequalities form, which can immensely reduce the computational complexity. Moreover, these results reveal the impact of the network system coefficient matrices on GAS and GMLS. Finally, in the three-dimensional space, the effectiveness and practicability of these synchronization criteria are confirmed by choosing different derivative orders and diffusion coefficients.

Impulsive Control and Synchronization for Fractional-Order Hyper-Chaotic Financial System

This paper reports a new global Mittag-Leffler synchronization criterion with regard to fractional-order hyper-chaotic financial systems by designing the suitable impulsive control and the state feedback controller. The significance of this impulsive synchronization lies in the fact that the backward economic system can synchronize asymptotically with the advanced economic system under effective impulse macroeconomic management means. Matlab’s LMI toolbox is utilized to deduce the feasible solution in a numerical example, which shows the effectiveness of the proposed methods. It is worth mentioning that the LMI-based criterion usually requires the activation function of the system to be Lipschitz, but the activation function in this paper is fixed and truly nonlinear, which cannot be assumed to be Lipschitz continuous. This is another mathematical difficulty overcome in this paper.

Extended analysis on the global Mittag-Leffler synchronization problem for fractional-order octonion-valued BAM neural networks

In this paper, a new case of neural networks called fractional-order octonion-valued bidirectional associative memory neural networks (FOOVBAMNNs) is established. First, the higher dimensional models are formulated for FOOVBAMNNs with general activation functions and the special linear threshold ones, respectively. On one hand, employing Cayley–Dichson construction in octonion multiplication which is essentially neither commutative nor associative, the system of FOOVBAMNNs is divided into four fractional-order complex-valued ones. On the other hand, Caputo fractional derivative’s character and BAM’s interactive feature are also properly dealt with. Second, the general criteria are obtained by the new design of LKFs, the application of the related inequalities and the construction of the linear feedback controllers for the global Mittag-Leffler synchronization problem of FOOVBAMNNs. Finally, we present two numerical examples to show the realizability and progress of the derived results.

Global Mittag-Leffler synchronization of discrete-time fractional-order neural networks with time delays

In this article, the problem of the global Mittag-Leffler synchronization is proposed for a sort of discrete-time fractional-order neural networks (DFNNs) with delays. In the first place, a flesh power law inequality pertaining to fractional difference is constructed by means of integration by parts, Young inequality, and some properties about fractional-order difference. In addition, based on aforesaid inequalities, Lyapunov function theory and properties of nabla Mittag-Leffler function as well as inequality techniques, some plentiful criteria are formed to achieve the global Mittag-Leffler synchronization for the delayed DFNNs via devising novel adaptive controller and delay feedback controller. In the end, numerical modeling is given to demonstrate effectiveness of theoretical verdicts.