Synchronization of recurrent neural networks with unbounded delays and time-varying coefficients via generalized differential inequalities.
In this paper, we revisit the drive-response synchronization of a class of recurrent neural networks with unbounded delays and time-varying coefficients, contrary to usual in the literature about time-varying neural networks, the signs of self-feedback coefficients are permitted to be indefinite or the time-varying coefficients can be unbounded. A generalized scalar delay differential inequality considering indefinite self-feedback coefficient and unbounded delay simultaneously is established, which covers the existing result with bounded delay, the applicabilities of the sufficient conditions are discussed. Some novel criteria for network synchronization are then derived by constructing different candidate functions. These results have been improved in some aspects compared with the existing ones. Differential inequality in vector form is also derived to obtain a more refined synchronization criterion which removes some strong assumptions. Three examples are presented to verify the effectiveness and show the superiorities of our theoretical results.
['Zhang|Hao|H|', 'Zeng|Zhigang|Z|']
D005246:Feedback D016571:Neural Networks, Computer D013997:Time Factors
Unbounded delay Time-varying neural networks Asymptotic synchronization Generalized delay differential inequality