Extended dissipative synchronization for singularly perturbed semi-Markov jump neural networks with randomly occurring uncertainties

This paper concentrates on the synchronization problem for singularly perturbed neural networks with semi-Markov jump parameters and randomly occurring uncertainties. A continuous-time semi-Markov process is utilized to model the stochastic switching of the parameters. An independent singularly perturbed parameter is separated through the use of singularly perturbed slow-fast decomposition method. Some sufficient conditions are deduced to ensure that the error system is synchronized and meets the extended dissipative property. In particular, the uncertainty of the networks is considered to occur randomly, which is more realistic than the existing work. Moreover, the efficiency of the presented method is demonstrated by a numerical example. (C) 2019 Elsevier B.V. All rights reserved.