Asymptotic synchronization of fractional order non-identical complex dynamical networks with Parameter Uncertainties

Abstract

This article specically deals with the asymptotic synchronization of non-identical complex dynamic fractional order networks with uncertainty. Initially, by using the Riemann-Liouville derivative, we developed a model for the general non-identical complex network, and based on the properties of fractional order calculus and the direct Lyapunov method in fractional order, we proposed that drive and response system if nonidentical complex networks ensuring asymp-totic synchronization by using neoteric control. Second, taking into account the uncertainties of non-identical complex networks in state matrices and evaluating theirrequirements forasymptotic synchronization. In addition, to explain the eectiveness of the proposed approach, two numerical simulations are given.

Social network learning: Uncertainty, heterogeneity, and the application in principal–agent relationships

“…Existing literature primarily focuses on naive learning or Bayesian learning [1, 17–19], or in fully connected networks [20], with limited exploration of gradual learning dynamics in incompletely connected networks. Our results show that individuals will converge on a common belief or take the same action through observational learning, which is consistent with what many studies report [1, 17‐19, 21, 22].…”