Global exponential periodicity of a class of recurrent neural networks with oscillating parameters and time-varying delays.
In this paper, we present the analytical results on the global exponential periodicity of a class of recurrent neural networks with oscillating parameters and time-varying delays. Sufficient conditions are derived for ascertaining the existence, uniqueness and global exponential periodicity of the oscillatory solution of such recurrent neural networks by using the comparison principle and mixed monotone operator method. The periodicity results extend or improve existing stability results for the class of recurrent neural networks with and without time delays.
Chen
Boshan
B
Department of Mathematics, Hubei Normal University, Huangshi, Hubei, China. chenbs@hbnu.edu.cn
Wang
Jun
J
D000818:Animals; D001683:Biological Clocks / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009991:Oscillometry / Q000379:methods; D010507:Periodicity; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors