惯性参考系
同步(交流)
人工神经网络
马尔可夫链
马尔可夫过程
应用数学
计算机科学
算法
数学
人工智能
拓扑(电路)
组合数学
机器学习
统计
物理
量子力学
作者
Jing Wang,Tingting Ru,Hao Shen,Jinde Cao,Ju H. Park
出处
期刊:IEEE Transactions on Network Science and Engineering
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号:8 (1): 163-173
被引量:27
标识
DOI:10.1109/tnse.2020.3032025
摘要
This paper investigates the finite-time synchronization issue for semi-Markov jump inertial neural networks, in which the sampled-data control is employed to alleviate the burden of the limited communication bandwidth. Due to the existence of inertial item, the semi-Markov jump inertial neural networks as hybrid neural systems, are depicted with second-order derivatives for the first time, which can be turned to first-order derivatives by the variable transformation. Furthermore, by applying appropriate integral inequalities and constructing the proper Lyapunov functional, some sufficient conditions, which not only guarantee the finite-time synchronization of the resulting error system but also ensure a specified level of L 2 - L ∞ performance, are acquired based on the optimization of integral inequality technique. A numerical example is, eventually, proposed to substantiate the validity of the developed method.
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