Finite-time nonfragile time-varying proportional retarded synchronization for Markovian Inertial Memristive NNs with reaction–diffusion items

Event‐triggered synchronisation of Markovian reaction–diffusion inertial neural networks and its application in image encryption

“…Therefore, compared with some papers that do not consider reaction–diffusion terms and the LKF that have triple and quadruple integral terms or just use Jensen inequality and some other simple manipulations, e.g. [12–16, 35, 42], the calculation in this paper is more complex.…”

“…All of the above factors imply that this kind of systems cannot be described by linear time‐invariant system. To ensure the accuracy of system modelling, in recent years, many scholars have adopted Markovian jump models to describe the complex systems [12–16, 33]. For another, any movement in the real world takes place in a certain spatiotemporal environment, such as wave propagation, chemical reaction process, food web etc., thus the mathematical model established with ignoring the spatial factors is not accurate.…”

“…In NNs, the latching phenomenon is common, which can be controlled effectively if we extract the finite‐state representation from the trained network. It has been proven that the switching between different NNs' modes can be governed by a Markov chain, which is suitable for modelling real systems with unpredicted abrupt changes in the structure and parameters, sudden environmental disturbances and component failures [12–15]. Thus recently, several results that combining INNs and Markovian jump parameters have been appeared, such as [16].…”

Synchronization for hybrid coupled reaction-diffusion neural networks with stochastic disturbances via spatial sampled-data control strategy

“…[9][10][11][12] Therefore, it is of great significance to study the synchronization of NNs. 13 In fact, in the electronic implementation of the simulated NNs, the communication and response of the neurons will be delayed due to the finite switching speed of the amplifier and the inherent communication time of neurons. It has been shown that the existence of time delay might affect the dynamical properties of the equilibrium points such as oscillation, divergence and even instability.…”

Event-Triggered Impulsive Fault-Tolerant Control for Memristor-Based RDNNs With Actuator Faults

“…In the control theory of memristor-based RDNNs, various control schemes have been proposed, which includes state feedback control [12], adaptive control [13], nonfragile control [15], and pinning control [18], [19]. The above-mentioned control methods are mainly using continuous-time feedback.…”