非线性系统
稳健性(进化)
上下界
激活函数
人工神经网络
趋同(经济学)
计算机科学
应用数学
木筏
功能(生物学)
控制理论(社会学)
数学
数学分析
物理
人工智能
化学
控制(管理)
生物
聚合物
经济
基因
进化生物学
共聚物
量子力学
生物化学
经济增长
核磁共振
作者
Fei Yu,Qiang Tang,Xiaoxue Li,Kenli Li,Shuo Cai
出处
期刊:Neurocomputing
[Elsevier]
日期:2019-07-01
卷期号:350: 108-116
被引量:164
标识
DOI:10.1016/j.neucom.2019.03.053
摘要
Nonlinear activation functions play an important role in zeroing neural network (ZNN), and it has be proved that ZNN can achieve finite-time convergence when the sign-bi-power (SBP) activation function is explored. However, its upper bound depends on initial states of ZNN seriously, which will restrict some practical applications since the knowledge of initial conditions is generally unavailable in advance. Besides, SBP activation function does not make ZNN reject external disturbances simultaneously. To address the above two issues encountered by ZNN, by suggesting a new nonlinear activation function, a robust and fixed-time zeroing neural dynamics (RaFT-ZND) model is proposed and analyzed for time-variant nonlinear equation (TVNE). As compared to the previous ZNN model with SBP activation function, the RaFT-ZND model not only converges to the theoretical solution of TVNE within a fixed time, but also rejects external disturbances to show good robustness. In addition, the upper bound of the fixed-time convergence is theoretically computed in mathematics, which is independent of initial states of the RaFT-ZND model. At last, computer simulations are conducted under external disturbances, and comparative results demonstrate the effectiveness, robustness, and advantage of the RaFT-ZND model for solving TVNE.
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