An LMI approach to global asymptotic stability of the delayed Cohen-Grossberg neural network via nonsmooth analysis.
In this paper, a linear matrix inequality (LMI) to global asymptotic stability of the delayed Cohen-Grossberg neural network is investigated by means of nonsmooth analysis. Several new sufficient conditions are presented to ascertain the uniqueness of the equilibrium point and the global asymptotic stability of the neural network. It is noted that the results herein require neither the smoothness of the behaved function, or the activation function nor the boundedness of the activation function. In addition, from theoretical analysis, it is found that the condition for ensuring the global asymptotic stability of the neural network also implies the uniqueness of equilibrium. The obtained results improve many earlier ones and are easy to apply. Some simulation results are shown to substantiate the theoretical results.
Yu
Wenwu
W
Department of Mathematics, Southeast University, Nanjing 210096, Jiangsu, China. wy2137@columbia.edu
Cao
Jinde
J
Wang
Jun
J
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016247:Information Storage and Retrieval; D016014:Linear Models; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated; D011382:Programming, Linear; D012815:Signal Processing, Computer-Assisted; D013598:Systems Theory