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In this paper, we study a projective multi-synchronization problem for fractional-order complex-valued coupled multi-stable neural networks (FCVCMNNs) with time-delays. Using a complex decomposition approach, FCVCMNNs are divided into their real and imaginary components. Our method uses certain conditions for each subnetwork to achieve the multiple local equilibrium points or stable periodic orbits that are exponentially stable, which, when combined with the Lyapunov functions method, result in FCVCMNNs that are projectively multi-synchronized. The FCVCMNNs, on the other hand, are examined directly through the use of the Lyapunov functional method and linear matrix inequality (LMI). Various new sufficient conditions in the form of complex-valued LMIs are presented for the projective multi-synchronization of the considered FCVCMNNs. As a final step, we provide two numerical simulations to verify the effectiveness of the main results derived in this paper. [ABSTRACT FROM AUTHOR]

• Distributed optimization problems of consensus are examined by a frequency-domain analysis approach. • An optimized distributed protocol is proposed such that the nonuniform time-delay FDMSs with SF have better consensus performance. • The double-integral dynamics of delayed FDMSs with SF is considered. This paper addresses distributed optimization problems concerning consensus in delayed fractional-order double-integrator multi-agent systems (FDMSs). To start with, an optimized distributed protocol with state-fractional-order-derivative feedback (SF) is presented for delayed FDMSs. Then, the consensus problems are studied for the two kinds of delayed FDMSs with SF in the presence of symmetric time-delays over undirected network topology and asymmetric time-delays over directed network topology. Next, by the means of graph theory, matrix theory and frequency-domain analysis method, the sufficient conditions to guarantee consensus of delayed FDMSs with SF are derived. Compared to the traditional distributed protocol without SF, the proposed distributed optimization protocol with SF are taken into account to enable better consensus performance in delayed FDMSs with SF. Finally, numerical experiments are carried out to verify the feasibility of our theoretical results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, a class of quaternion-valued BAM neural networks (QVBAMNNs) fractional-order model with impulses and proportional delays is proposed in discrete-time case. The QVBAMNNs fractional-order model is investigated directly rather than through real decomposition method or the plural one. By employing homeomorphic mapping theorem, Lyapunov stability theory and inequality technology, several criteria for the discrete-time QVBAMNNs fractional-order model are derived to guarantee the existence, uniqueness and global exponential stability (GES) of equilibrium point. The novelty of these results comes from the generality with the fractional-order systems and the integer-order ones. Finally, two examples are given to demonstrate the effectiveness and availability of the derived criteria. [ABSTRACT FROM AUTHOR]

Taking into account fractional-order complex-valued neural networks with time-varying delay, the issue of achieving fixed-time synchronization is discussed in this paper. By utilizing the properties of fractional calculus and fractional-order comparison principle, an improved lemma is proposed to derive the fixed-time synchronization conditions. On the basis of sliding model control and Lyapunov stability theorem, an effective sliding mode surface is constructed, which only uses the synchronization error information of FOCVNNs and is composed of fractional and integer integral terms. Further, a suitable sliding model control is constructed, which makes synchronization error converges to zero in a fixed-time. Beyond that, several sufficient conditions are posed to guarantee fixed-time synchronization of the fractional-order complex-valued neural networks and the upper bound of synchronization settling time is estimated. Finally, two numerical simulations are given to demonstrate the effectiveness of the presented theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, without dividing the fractional-order complex-valued coupled neural networks (FCVCNNs) into two real-valued systems, the problem of synchronization is investigated for FCVCNNs with time-varying coupling strength. To achieve synchronization, by updating coupling strength, two feasible adaptive protocols are designed, 1) a fractional-order adaptive strategy depending on global information; 2) a fractional-order adaptive law relying on the local information based on the connected dominating set theory. Additionally, instead of making the weighted average technique or the solution of the isolated node equation as synchronization reference value, direct error method is adopted to realize synchronization, which offers a flexible way in analysis of FCVCNNs. At last, a numerical simulation is provided to illustrate the validity of theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the stability for a class fractional-order inertial neural networks with time-delay are investigated. Moreover, some sufficient conditions for the Mittag-Leffler stability and the asymptotical ω -periodicity are obtained, by the appropriate transformation, using the property of the Riemann-Liouville fractional integral and derivative. In the end, results of the theoretical derivation are verified by virtue of two numerical simulation examples. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, under the framework of Filippov solutions, quasi-synchronization issue of fractional-order complex-valued neural networks (FCNNs) with discontinuous activations and uncertainties is investigated. Firstly, using Laplace transform and the property of Mittag-Leffler function, a novel fractional differential inequality is derived. Then combining the newly constructed inequality with delay feedback control scheme, several quasi-synchronization conditions are obtained for the considered FCNNs by means of non-decomposable method. Eventually, a numerical example is provided to substantiate validation of the proposed results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stability and Hopf bifurcation for a fractional-order Susceptible-Vaccinated-Exposed-Infectious-Recovered (SVEIR) computer virus propagation model with a nonlinear incident rate. By employing the linearization technique and Routh-Hurwitz method, the sufficient criterion is established for the locally asymptotic stability of endemic equilibrium point. The Hopf bifurcation is also studied for the SVEIR computer virus model by taking time delay as the bifurcation parameter. The research results show that the stability and Hopf bifurcation of proposed model are significantly affected by both time delay and the order of the fractional derivative. Examples with proper parameters and several simulations are given to illustrate the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the stabilization control of the memristive neural networks with discrete-time terms and fractional derivative is proposed. By employing the Lyapunov functional method and fully considering the integrity of the quaternion system, some sufficient criteria are derived based on nonlinear scalarization approach and several inequality techniques. The stabilization criteria are established in the form of algebraic inequality, which can be directly resolved by a simple calculation. Finally, two numerical examples are proposed to demonstrate the validity of theoretical results. [ABSTRACT FROM AUTHOR]