Gunasekaran Nallappan, Thoiyab N Mohamed, Zhu Quanxin, Cao Jinde, Muruganantham P
IEEE Trans Cybern. 2022 Nov;52(11):11794-11804. doi: 10.1109/TCYB.2021.3079423. Epub 2022 Oct 17.
This article identifies a new upper bound norm for the intervalized interconnection matrices pertaining to delayed dynamical neural networks under the parameter uncertainties. By formulating the appropriate Lyapunov functional and slope-bounded activation functions, the derived new upper bound norms provide new sufficient conditions corresponding to the equilibrium point of the globally asymptotic robust stability with respect to the delayed neural networks. The new upper bound norm also yields the optimized minimum results as compared with some existing methods. Numerical examples are given to demonstrate the effectiveness of the proposed results obtained through the new upper bound norm method.
本文针对参数不确定情况下的时滞动态神经网络，确定了区间化互联矩阵的一种新的上界范数。通过构造适当的李雅普诺夫泛函和斜率有界激活函数，所推导的新上界范数为与时滞神经网络全局渐近鲁棒稳定性平衡点相对应的新充分条件。与一些现有方法相比，新上界范数还产生了优化的最小结果。给出了数值例子以证明通过新上界范数方法获得的所提结果的有效性。
