Multistability and new attraction basins of almost-periodic solutions of delayed neural networks.
In this paper, we investigate multistability of almost-periodic solutions of recurrently connected neural networks with delays (simply called delayed neural networks). We will reveal that under some conditions, the space R(n) can be divided into 2(n) subsets, and in each subset, the delayed n-neuron neural network has a locally stable almost-periodic solution. Furthermore, we also investigate the attraction basins of these almost-periodic solutions. We reveal that the attraction basin of almost-periodic trajectory is larger than the subset, where the corresponding almost-periodic trajectory is located. In addition, several numerical simulations are presented to corroborate the theoretical results.
Wang
Lili
L
Shanghai Key Laboratory for Contemporary Applied Mathematics and Key Laboratory of Nonlinear Science of Chinese Ministry of Education, School of Mathematical Sciences, Fudan University, Shanghai 200433, China. 042018047@fudan.edu.cn
Lu
Wenlian
W
Chen
Tianping
T
D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009991:Oscillometry / Q000379:methods*; D010507:Periodicity; D012815:Signal Processing, Computer-Assisted