一类具变时滞的非自治模糊BAM神经网络周期解的全局指数稳定性

摘要

研究一类在以往文献中很少提及的具变时滞的非自治模糊BAM (bi-directional associative memory)神经网络.通过构造Lyapuonv函数,利用M-矩阵理论以及Yang不等式等分析技巧,给出非自治模糊BAM神经网络周期解的全局指数稳定性的充分条件,这些条件去掉了对激活函数的有界性、单调性和可微性的要求,且在某些情况下更易验证.最后通过一个例子验证了所给结果的有效性.%A class of non-autonomous fuzzy BAM(bi-directional associative memory) neural network with varying-time delay, which rarely mentioned in the literatures available, is investigated.By constructing Lyapunov function, applying such analysis skill as M-matrix theory and Young inequality, a sufficient condition is given for the global exponential stability of periodic solutions of a class of non-autonomous fuzzy BAM neural network with varying-time delay, and the requirement of boundedness, monotonieity, and differentiability to the activation functions will be avoided due to this condition, and in some cases, the stability criteria can be easily checked and verified.Finally, one example is given to verify the effectiveness of the result obtained.