Parameter Identification of Memristor-Based Chaotic Systems via the Drive-Response Synchronization Method

Graph‐theoretic approach to finite‐time synchronization and topology identification of delayed multi‐group models with stochastic perturbation and multiple dispersal

“…For example, in an ecological environment, the habitats of many biological species can be divided into several patches due to the spatial heterogeneity and the increasing spread of human activities [5]. In epidemiology, with the spread of novel coronavirus disease 2019 (COVID- 19) around the world, a heterogeneous host population can be divided into several homogeneous groups in the process of controlling the number of COVID-19 infections [6]. Meanwhile, multiple dispersal among groups are inevitable.…”

“…In the previous researches, various methods are proposed to identify the topological structures and parameters of complex dynamical networks, including the compressive sensing technique [15], adaptive fuzzyimpulsive control approach [16], and so forth. Among these various approaches, the synchronization-based method is of high concern [17][18][19], because it is a simple approach to implement for estimating the detailed number of dispersal and the magnitude of the dispersal strength.…”

Parameters identification for the nonlinear systems via target system-based method with intermittent adjustment

“…However, models established for specific problems often have some unknown parameters, so it is crucial to identify the unknown parameters in the models with the help of experimental data. Many methods existing in current researches for parameters identification are based on the stability theory of dynamical systems, such as the synchronization method based on the LaSalle's principle [16,10,28], Lyapunov stability method [28], adaptive method [26,35,27] and many other methods [6,22,24]. In order to realize the identification of unknown parameters of a system, synchronization will be an intermediate step generally.…”