一类中立型比例时滞神经网络的全局渐近镇定性  

Global asymptotic stabilization of a class of neutral proportional delayed neural networks

作　　者：贾舒伊 周立群[1] 赵丹华 JIA Shuyi;ZHOU Liqun;ZHAO Danhua(College of Mathematical Science,Tianjin Normal University,Tianjin 300387,China;College of Software,Tianjin Normal University,Tianjin 300387,China)

机构地区：[1]天津师范大学数学科学学院,天津300387 [2]天津师范大学软件学院,天津300387

出　　处：《天津师范大学学报（自然科学版）》2022年第6期1-7,共7页Journal of Tianjin Normal University：Natural Science Edition

基　　金：天津市自然科学基金资助项目(18JCYBJC85800);天津市普通高等学校本科教学质量与教学改革研究计划资助项目(B201006505);天津师范大学研究生科研创新(重点)资助项目(2022KYCX035Z)。

摘　　要：研究一类中立型比例时滞神经网络的全局渐近镇定性.首先,采用状态反馈控制器,利用同胚映射定理,分析了控制器作用下的系统平衡点的存在唯一性;其次,通过一个非线性变换将比例时滞神经网络变换为等价的常时滞变系数神经网络;然后,通过构造Lyapunov泛函和运用不等式分析技巧,得到系统全局渐近镇定性的判定准则;最后,通过数值算例及其仿真验证了所得结论的有效性.The global asymptotic stabilization of a class of neutral proportional delayed neural networks is studied.Firstly,by taking a state feedback controller and using the homeomorphic mapping theorem,the existence and uniqueness of the equilibrium for the system under the action of the controller is analyzed.Next,through a nonlinear transformation,the class of proportional delay neural networks is equivalently transformed into the class of neural networks with constant time delay and variable coefficients.And then,the criterion that the system is globally asymptotically stabilized is obtained by constructing a proper Lyapunov functional and using inequality analysis techniques.Finally,a numerical example and its simulations are used to verify the effectiveness of the results.

关 键 词：中立型神经网络 比例时滞 LYAPUNOV泛函 全局渐近镇定性 

分 类 号：O175.13[理学—数学]