Finite-time reliable sampled-data control for fractional-order memristive neural networks with quantisation

New Results on Robust Synchronization for Memristive Neural Networks with Fractional Derivatives via Linear Matrix Inequality

“…In recently years, the dynamical behavior of fractional-order neural networks (FONN) was widely studied in [22][23][24][25][26][27][28][29][30], especially fractional-order memristive neural networks (FOMNNs) [31][32][33][34][35][36][37][38][39][40][41]. Chen et al investigated Mittag-Leffler synchronization of a FOMNN by using an M-matrix method and set-valued theory in [31].…”

“…The rich dynamic behaviors with different fractional-orders were proposed, such as complete synchronization, lag synchronization, phase synchronization, and sine-like synchronization, when the neurons are locally and diffusively coupled in a ring topology. In [39][40][41], finite time stability, finite time synchronization, and finite time Mittag-Leffler synchronization were extensively investigated. Some new sufficient criteria in the frame of linear matrix inequalities to assure the finite-time stability of FOMNNs were presented subject to the quantisation phenomenon, actuator failures, and time-varying delay by sampled-data control in [39].…”

“…In [39][40][41], finite time stability, finite time synchronization, and finite time Mittag-Leffler synchronization were extensively investigated. Some new sufficient criteria in the frame of linear matrix inequalities to assure the finite-time stability of FOMNNs were presented subject to the quantisation phenomenon, actuator failures, and time-varying delay by sampled-data control in [39]. Some results on asymptotic stability and finite-time stability were constructed by using the theory of fractional calculus for a general class of delayed inertial FOMNN [40].…”

“…One is normbounded uncertainty, and the other is bounded real uncertainty; see [42]. Differently from reference [26,27,36,37,39], a FOMNN with norm-bounded uncertainty is studied for the first time in this article.…”

Power-exponential and fixed-time consensus of conformable fractional-order quantum cellular neural networks via event-triggered control

“…As an important dynamic behavior of chaotic systems, synchronization exists widely in application areas such as cryptographic construction systems, fault diagnosis systems, multiuser detection systems, and image watermarking systems [28][29][30][31][32][33][34][35][36][37]. During the past several years, various kinds of synchronization have been proposed in control science, such as asymptotic synchronization and finite-time synchronization.…”