IEEE Trans Neural Netw Learn Syst. 2021 Jul;32(7):3240-3246. doi: 10.1109/TNNLS.2020.3008691. Epub 2021 Jul 6.
This brief studies the problem of extended dissipativity analysis for the Markovian jump neural networks (MJNNs) with time-varying delay. A double-integral-based delay-product-type (DIDPT) Lyapunov functional is first constructed in this brief, which makes full use of the information of time delay. Moreover, some unnecessary constraints on the system structure are removed, which leads to more general results. A numerical example is employed to illustrate the advantages of the proposed method.
本研究探讨了时变时滞马尔可夫跳变神经网络（MJNNs）的扩展耗散性分析问题。本研究首次构建了基于双积分的时滞积型（DIDPT）李雅普诺夫函数，充分利用了时滞信息。此外，还消除了对系统结构的一些不必要的约束，从而得到了更一般的结果。通过数值例子说明了所提出方法的优势。
