摘要
针对时滞神经网络模型,研究了间歇型的非脆弱状态估计问题。所考虑的估计器可以间断地工作,并且其控制参数满足范数有界的不确定性。首先,借助Lyapunov稳定性和矩阵不等式,给出了间歇型非脆弱估计器的存在条件,指出当估计器的停歇率在一定范围内,误差系统是鲁棒指数稳定的。接着,估计器的增益矩阵通过线性矩阵不等式的可行解表示。最后,通过数值举例验证了所得结果的可行性。
In this paper,the intermittently non-fragile state estimation problem is studied for a class of time-delayed neural network model.The estimator under consideration can operate intermittently,and its control parameter satisfies the norm bounded uncertainty.Firstly,by means of Lyapunov stability and matrix inequalities,some sufficient conditions are given to guarantee the existence of intermittently non-fragile estimator.The result shows that the error system is robustly exponential stability if the intermittent rate is within a certain range.Then,the gain matrix can be expressed by the feasible solutions of the linear matrix inequality.Finally,a numerical example is given to verify the feasibility of the obtained results.
作者
崔颖
张鑫烨
CUI Ying;ZHANG Xinye(School of Mathematics and Statistics,Fuyang Normal University,Fuyang Anhui 236037,China)
出处
《阜阳师范大学学报（自然科学版）》
2021年第2期1-5,共5页
Journal of Fuyang Normal University:Natural Science
基金
国家自然科学基金项目(62003090)
安徽省自然科学基金青年项目(2008085QA16)
安徽省教育厅高校科学研究重点项目(KJ2019A0528,KJ2019A0543)
国家级大学生创新创业训练计划项目(202010371005)资助。
关键词
时滞神经网络
间歇
非脆弱
状态估计
delayed neural network
intermittent
non-fragile
state estimation
