On periodic solutions of neural networks via differential inclusions.
Discontinuous dynamical systems, especially neural networks with discontinuous activation functions, arise in a number of applications and have received considerable research attention in recent years. However, there still remain some fundamental issues to be investigated, for instance, how to define the solutions of such discontinuous systems and what conditions can guarantee the existence and stability of the solutions. In this paper, based on the concept of Filippov solution, the dynamics of a general class of neural networks with discontinuous activation functions is investigated. Sufficient conditions are obtained to ensure the existence and stability of the unique periodic solution for the neural networks by using the differential inclusions theory, the Lyapunov-Krasovskii functional method and linear matrix inequality (LMI) technique. Two numerical examples are given to illustrate the theoretical results.
Liu
Xiaoyang
X
Department of Mathematics, Southeast University, Nanjing 210096, China.
Cao
Jinde
J
D000465:Algorithms; D003198:Computer Simulation; D008433:Mathematics; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D013598:Systems Theory