高师理科学刊2024，Vol.44Issue(6)：6-10,5.DOI:10.3969/j.issn.1007-9831.2024.06.002
求解常微分方程的两类零化神经网络
Two types of zeroing neural networks for solving ordinary differential equations
摘要
Abstract
Two types of zeroing neural networks are proposed for solving ordinary differential equations.By rewriting ordinary differential equations into vector valued uncertain error functions,substituting vector valued uncertain error functions into the zeroing neural network design formula,a class of continuous time zeroing neural networks for solving ordinary differential equations is proposed,which converges to zero exponentially.For potential digital hardware realization,a class of discrete-time neural networks was designed by discretizing the continuous time zeroing neural network.At the same time,sufficient and necessary conditions are provided to ensure that the sequence generated by the discrete-time neural network converges to zero with a truncation error of O(τ2)(τ>0 denoting the sampling period).The effectiveness of continuous time zeroing neural networks and discrete-time zeroing neural networks was verified through two numerical experiments.关键词
零化神经网络/常微分方程/截断误差Key words
zeroing neural network/ordinary differential equations/truncation error分类
数理科学引用本文复制引用
孙敏,田茂英,郭玉霞,刘巧莲..求解常微分方程的两类零化神经网络[J].高师理科学刊,2024,44(6):6-10,5.基金项目
枣庄学院博士科研启动基金项目 （）
枣庄学院大学生创新创业项目 （）
山东省自然科学基金项目(ZR2022MA081) （ZR2022MA081）
