摘要
本文研究了具有事件触发和量化的时滞神经网络系统状态估计问题.为减轻网络负荷,本文考虑了基于数据动态传输的事件触发机制以及量化控制,在此基础上对时滞神经网络系统的状态估计器设计问题进行研究.充分考虑网络对系统性能的影响,首先建立基于事件触发和量化控制的时滞神经网络系统状态估计数学模型,然后通过利用Lyapunov稳定性理论和线性矩阵不等式技术,分别给出基于事件触发和量化作用下系统渐近稳定和状态估计器存在的充分条件,最后通过数值实例验证本文所提方法的有效性.
This paper presents an investigation of the state estimation problem for a class of delayed neural network systems with event-triggered communication and quantization.The network bandwidth burden is reduced by using both an event-triggered communication scheme and quantization with which the state estimator design for delayed neural network systems is concerned.Considering the influence of the communication network,an event-based state estimator error dynamic model for delayed neural network systems is firstly constructed by taking the effect of the event-triggered scheme and quantization into consideration.Then by employing the Lyapunov functional approach and the linear matrix inequality technique,some sufficient conditions are obtained under which the state estimator exists and the estimator error dynamics is asymptotically stable.Finally,a numerical example is provided to demonstrate the usefulness of the proposed approach.
出处
《中国科学：信息科学》
CSCD
北大核心
2016年第11期1555-1568,共14页
Scientia Sinica(Informationis)
基金
国家自然科学基金(批准号:61403185)
江苏省属高校自然科学研究重大项目(批准号:15KJA120001)
江苏省"六大人才高峰"资助项目(批准号:2015-DZXX-021)
江苏省高校"青蓝工程"优秀青年骨干教师培养项目(2014)
江苏高校优势学科建设工程资助项目(PAPD)资助
关键词
神经网络
事件触发
量化
状态估计
网络控制系统
neural networks
event-triggered scheme
quantization
state estimation
networked control systems
