An Improved Result on Sampled-Data Synchronization of Markov Jump Delayed Neural Networks
Hao Yang Shen, Shiyu Jiao, Jinde Cao, Tingwen Huang · IEEE Transactions on Systems Man and Cybernetics Systems · 2019
This article deals with the issue of dissipative synchronization for Markov jump neural networks subject to time-varying interval delays by using the sampled-data control method. First, a new two-sided looped-functional is introduced to improve the informativeness of the formed Lyapunov-Krasovskii functional, which takes into an account the information of the entire sampling period. Compared with some traditional methods, the information between x(t) and x(tk+1) is also emphasized. On this basis, an improved inequality technique is considered valid to acquire the less conservative synchronization criterion. In the end, two numerical examples are displayed, and a comparative example powerfully demonstrates the availability and the superiority of the developed technique.