Cluster synchronization of coupled complex-valued neural networks with leakage and time-varying delays in finite-time

In cluster synchronization (CS), the constituents (i.e., multiple agents) are grouped into a number of clusters in accordance with a function of nodes pertaining to a network structure. By designing an appropriate algorithm, the cluster can be manipulated to attain synchronization with respect to a certain value or an isolated node. Moreover, the synchronization values among various clusters vary. The main aim of this study is to investigate the asymptotic and CS problem of coupled delayed complex-valued neural network (CCVNN) models along with leakage delay in finite -time (FT). In this paper, we describe several sufficient conditions for asymptotic synchronization by utilizing the Lyapunov theory for differential systems and the Filippov regularization framework for the realization of finite-time synchronization of CCVNNs with leakage delay. We also propose sufficient conditions for CS of the system under scrutiny. A synchronization algorithm is developed to indicate the usefulness of the theoretical results in case studies.

Automated summary

This study investigates the asymptotic and cluster synchronization issues of coupled delayed complex-valued neural network models with leakage delay in finite time. Several sufficient conditions for asymptotic synchronization and finite-time synchronization are described utilizing the Lyapunov theory and the Filippov regularization framework.