Quasi-Synchronization and Dissipativity Analysis for Fractional-Order Neural Networks with Time Delay

The objective of this research is to examine the global dissipativity and quasi-synchronization of fractional-order neural networks (FNNs). A global dissipativity criterion is established through the creation of an appropriate Lyapunov function, together with some fractional-order inequality techniques. Additionally, the issue of quasi-synchronization for drive-response FNNs is investigated using linear state feedback control. The study reveals the synchronization error converges to a bounded region by choosing an appropriate control parameter. Finally, the effectiveness of the obtained works are validated through three numerical examples.

Automated summary

The research aims to investigate the global dissipativity and quasi-synchronization of fractional-order neural networks (FNNs). A criterion for global dissipativity is established by creating an appropriate Lyapunov function and using fractional-order inequality techniques. Additionally, the issue of quasi-synchronization in drive-response FNNs is studied using linear state feedback control. The study demonstrates that the synchronization error converges to a bounded region by selecting an appropriate control parameter. The effectiveness of the research is validated through three numerical examples.