摘要
对带有时滞的模糊随机BAM神经网络进行了固定时间同步分析.在传统BAM神经网络的框架下,引入了随机扰动、模糊性、离散时滞的影响,使得网络模型更符合实际系统,拓展了模型的应用范围.在此基础上,通过设计合适的反馈控制器,利用Lyapunov方法,得到了系统达到固定时间同步的判定准则.最后进行了数值模拟验证理论结果的有效性.
Fixed-time synchronization analysis was performed for fuzzy stochastic BAM neural networks with time delay. In the framework of traditional BAM neural network, the effects of random perturbation, fuzziness, and discrete time delay were introduced to make the network model more consistent with the actual system and expanded the application range of the model. On this basis, by designing a suitable feedback controller and using Lyapunov method, the determination criterion for the system to reached fixed time synchronization was obtained. Finally, numerical simulations were conducted to verify the validity of the theoretical results.
作者
刘宇
刘铭
LIU Yu;LIU Ming(College of Science,Northeast Forestry University,Harbin 150040,China)
出处
《哈尔滨商业大学学报（自然科学版）》
CAS
2022年第1期75-81,共7页
Journal of Harbin University of Commerce：Natural Sciences Edition
基金
教育部高校基本科研业务费专项资金资助项目(2572020BC09)。
关键词
BAM神经网络
随机扰动
固定时间同步
反馈控制
离散时滞
模糊性
BAM neural network
sochastic perturbation
fixed-time synchronization
feedback control
discrete time delay
fuzzy
