New Cluster Synchronization Criteria for Markov Jump Coupled Neural Networks of Neutral-Type with Unknown Transition Rates

Background: Neural networks have been very successful in various sorts of aspects, such as computer vision, intelligent prediction and control, image processing, and natural language processing. Coupled delayed neural networks of neutral-type possess more sophisticated actions than a single node neural network. Nevertheless, it is still a challenge to consider the synchronization issue of coupled neural networks of neutral-type processed unknown transition rates. Method: The authors in this paper propose an augmented Lyapunov-Krasovskii functional and use further improved integral inequality and Dynkin's formula to acquire the delay-dependent cluster synchronization criteria that comprise upper and lower bounds of delays, which have less conservative criteria than the existing results. Meanwhile, the transition rates in the Markov jump coupled delayed neural networks of neutral-type which are not fully known and the coupling configuration matrices are not confined to symmetry. Hence, the matrices in this system have less constrained conditions than most of the existed papers. Result: By applying the infinitesimal operator and the special matrix K , the newly delaydependent cluster synchronization criteria are presented for the Markov jump coupled neural networks of neutral-type with unknown transition rates. Conclusion: The less conservative cluster synchronization criteria have been proposed for Markov jump coupled neutral-type networks with unknown transition rates by adopting the augmented Lyapunov- Krasovskii functional and integral inequality. A numerical example has been provided to illustrate the effectiveness of the proposed approach. The method in this paper is less conservative than the existed ones.