Global exponential synchronization of complex-valued recurrent neural networks in presence of uncertainty along with time-varying bounded and unbounded delay terms

In this article, the global exponential synchronization criteria of the complex-valued recurrent neural networks (CVRNNs) in the presence of uncertain parameters with time-varying bounded and unbounded delay terms have been investigated. Based on Halanay inequality and matrix measure approach, the global exponential synchronization is studied for two cases. The first case is the synchronization of CVRNNs in the presence of uncertain parameters with time-varying bounded and unbounded delay terms and second one is the concerned synchronization in the absence of uncertain terms with same bounded and unbounded time-varying delay terms. The synchronization of the addressed complex-valued neural networks is achieved with the help of Lyapunov functional, and several sufficient criteria and theorems. Finally, two numerical examples are taken to show the viability and unwavering quality of our theoretical results for various cases.