This paper considers the drive-response synchronization in finite-time and fixed-time of inertial neural networks with time-varying and distributed delays(mixed delays). First, by constructing a proper variable substi...This paper considers the drive-response synchronization in finite-time and fixed-time of inertial neural networks with time-varying and distributed delays(mixed delays). First, by constructing a proper variable substitution, the original inertial neural networks can be rewritten as a first-order differential system. Second, by constructing Lyapunov functions and using differential inequalities,some new and effective criteria are obtained for ensuring the finite-time synchronization. Finally, three numerical examples are also given at the end of this paper to show the effectiveness of the results.展开更多
This paper investigates the polynomial synchronization(PS)problem of complex-valued inertial neural networks with multi-proportional delays.It is analyzed based on the non-separation method.Firstly,an exponential tran...This paper investigates the polynomial synchronization(PS)problem of complex-valued inertial neural networks with multi-proportional delays.It is analyzed based on the non-separation method.Firstly,an exponential transformation is applied and an appropriate controller is designed.Then,a new sufficient criterion for PS of the considered system is derived by the Lyapunov function approach and some inequalities techniques.In the end,a numerical example is given to illustrate the effectiveness of the obtained result.展开更多
文摘This paper considers the drive-response synchronization in finite-time and fixed-time of inertial neural networks with time-varying and distributed delays(mixed delays). First, by constructing a proper variable substitution, the original inertial neural networks can be rewritten as a first-order differential system. Second, by constructing Lyapunov functions and using differential inequalities,some new and effective criteria are obtained for ensuring the finite-time synchronization. Finally, three numerical examples are also given at the end of this paper to show the effectiveness of the results.
基金the National Natural Science Foundation of China (61503222, 62173214)the Natural Science Foundation of Shandong Province of China (ZR2021MF100)+2 种基金the Research Fund for the Taishan Scholar Project of Shandong Province of Chinain part by the Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China (2019KJI005)in part by the SDUST Research Fund
文摘This paper investigates the polynomial synchronization(PS)problem of complex-valued inertial neural networks with multi-proportional delays.It is analyzed based on the non-separation method.Firstly,an exponential transformation is applied and an appropriate controller is designed.Then,a new sufficient criterion for PS of the considered system is derived by the Lyapunov function approach and some inequalities techniques.In the end,a numerical example is given to illustrate the effectiveness of the obtained result.
