Revealing network topology and dynamical parameters in delay-coupled complex network subjected to random noise

Abstract

It is well known that random noise and time delay are two inherent ingredients in complex networks, whose dynamical parameters and topological structures are often unknown or uncertain. This paper will employ the techniques of impulsive control and adaptive control to infer dynamical parameters and network topology in delay-coupled complex network under circumstance noise. By constructing an appropriate adaptive-impulsive control strategy in the response network, the unknown dynamical parameters and topology structure contained in the drive network are to be accurately identified; moreover, these two networks will achieve the global exponential synchronization in mean square. Based on the comparison theorem of impulsive differential equations, the accuracy of the proposed identification strategy is rigorously proved. Finally, two examples with networks of chaotic oscillators are presented to illustrate the application of the suggested strategy. Meanwhile, numerical results indicate that our proposed scheme is robust against the impulsive gain, the update gain and the network topology.

Local time-varying topology identification of network with unknown parameters based on adaptive synchronization

“…Li et al [13] and Timme [14] identified the phase oscillator network topology by repeating experiments many times based on phase synchronization, which needs a large amount of calculation for large-scale networks. Yang and Wei [15] and Liu et al [16] introduced the impulse control strategy for network topology identification to save the control energy. Stanković et al [17] proposed to estimate all system parameters utilizing a stochastic approximation algorithm with the expanding truncation derived from the modified Yule-Walker equations.…”

Recovery of couplings and parameters of elements in networks of time-delay systems from time series

“…A limitation of this approach is the necessity of disturbing oscillators in the network. Some methods exploit an auxiliary response network with the same intrinsic dynamics of the individual nodes as the drive network under study [6][7][8]. These methods use adaptive feedback control for synchronizing the drive and response networks, thus estimating the network connectivity.…”

Adaptive Control of Dynamically Complex Networks with Saturation Couplings

“…It is regarded as a fundamental tool in understanding a variety of dynamic phenomena which are presented in real worlds [1,2]. During the past years, a lot of related problems have been investigated for complex networks, for example, stability and synchronization [3][4][5][6][7], the analysis of complex networks with topological structures [8][9][10][11], pinning control [12][13][14], adaptive control [15][16][17], impulsive control [18,19], and robust analysis [20][21][22].…”