非线性系统
人工神经网络
趋同(经济学)
应用数学
数学
上下界
符号(数学)
理论(学习稳定性)
计算机科学
控制理论(社会学)
数学分析
物理
人工智能
经济增长
量子力学
机器学习
经济
控制(管理)
作者
Lin Xiao,Wenxin Song,Lei Jia,Xiaopeng Li
标识
DOI:10.1016/j.neucom.2022.05.067
摘要
Since the solution of time-variant nonlinear inequality systems is trapped by the convergence performance of the models, this paper explores an enhanced nonlinear sign-bi-power activation function (AF) and further obtains a zeroing neural network (ZNN) model for solving time-variant nonlinear inequality systems, which is called nonlinear activated finite-time convergent zeroing neural network (NAFTCZNN). The strict theoretical analysis together with two theorems are given to demonstrate the enhanced convergence performance of the NAFTCZNN model. Furthermore, the stability and upper bound of convergent time of the NAFTCZNN model are analyzed and estimated in the theorems, which is more stable and less conservative than the ZNN models using the common sign-bi-power AFs. Numerical example results further validate the effectiveness and excellence of the NAFTCZNN model in terms of solving the time-variant nonlinear inequality systems.
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