Global exponential stability of bidirectional associative memory neural networks with time delays.
In this paper, we consider delayed bidirectional associative memory (BAM) neural networks (NNs) with Lipschitz continuous activation functions. By applying Young's inequality and Hoelder's inequality techniques together with the properties of monotonic continuous functions, global exponential stability criteria are established for BAM NNs with time delays. This is done through the use of a new Lyapunov functional and an M-matrix. The results obtained in this paper extend and improve previous results.
Liu
Xin-Ge
XG
School of Mathematical Science and Computing Technology, Central South University, Changsha, Hunan 410083, China liuxgliuhua@163.com
Martin
Ralph R
RR
Wu
Min
M
Tang
Mei-Lan
ML
D000465:Algorithms; D006801:Humans; D008568:Memory; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors