密尔内-哈姆的方法与零神经网络的时间变异非线性优化和冗余的操纵器应用程序.
概括
一个新的Milne-Hamming离散归零神经网络 (DZNN) 模型改进了时间变化的非线性优化. 这种方法为复杂问题提供了比现有的DZNN模型更高的稳定性和准确性.
科学领域:
- 计算数学是指计算数学.
- 神经网络优化神经网络优化
- 应用数学 应用数学 应用数学
背景情况:
- 为了优化,建立了连续和离散的零化神经网络 (ZNN).
- 现有的离散ZNN (DZNN) 模型在复杂问题的稳定性和准确性方面存在局限性.
研究的目的:
- 提出和分析一个新的米尔内-哈明离散ZNN (MHDZNN) 模型.
- 解决具有功能限制的时间变异非线性优化 (TV-NO) 问题.
主要方法:
- 使用四步米尔内-哈明 (MH) 方法对ZNN模型进行分离.
- 对于绝对稳定性和误差趋同的MHDZNN模型的理论分析.
- 数字模拟和对冗余操纵器的应用.
主要成果:
- 该MHDZNN模型显示了一个$\mu \in (0,1/2) $的扩展绝对稳定性域.
- 达到 $O{\tau ^{5}) $ 顺序的收误差,截断误差常数为 $1/40$.
- 在收误差和稳定性域方面,优于现有的显式DZNN模型.
结论:
- 提出的MHDZNN模型为TV-NO问题提供了卓越的准确性和稳定性.
- MH方法有效地对ZNN进行分离,以提高性能.
- 通过数值模拟和实际应用来验证有效性.
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