基于划分时滞区间的一类Cohen-Grossberg神经网络的鲁棒稳定性

Robust stability for a class of Cohen-Grossberg neural networks by dividing delay interval

研究一类具有时变时滞及参数不确性的Cohen-Grossberg神经网络的鲁棒稳定性问题.应用划分时滞区间的思想构造了一个新的Lyapunov泛函,并以线性矩阵不等式的形式给出了平衡点全局鲁棒稳定性判据,新判据放松了时变时滞变化率必须小于1的限制.仿真结果进一步证明了所得结论的有效性.

Robust stability of a class of Cohen-Grossberg neural networks with time-varying delay and parameter uncertainty is studied. An idea of dividing delay interval is used. New robust stability criteria based on this idea is derived by construsting a new Lyapunov functional. Moreover, the restriction of the time derivative of time-varying delay is released in the proposed criteria. The simulation result verifies the effectiveness of the proposed criteria.