New Fixed-Time Stability Lemmas and Applications to the Discontinuous Fuzzy Inertial Neural Networks
Fanchao Kong, Quanxin Zhu, Tingwen Huang · IEEE Transactions on Fuzzy Systems · 2020
This article aims to analyze the fixed-time synchronization of a class of discontinuous fuzzy inertial neural networks with time-varying delays based on the new improved fixed-time stability lemmas. First of all, by using the generalized variable transformation and Filippov solution theory, the discontinuities of the considered neural system can be coped with, and the error system is established. By relaxing the conditions of the$C$-regular Lyapunov function, two new fixed-time stability lemmas are proved via simple inequality techniques. The setting times are also estimated and are more accurate in comparison with the previous ones. Finally, one typical numerical example is carried out to verify the correctness and the advantages of the main results.