一个动态增益 固定的时间 强大的 ZNN 模型 时间变量等式 约束的四次数最小平方 问题 与应用到多代理系统
IEEE transactions on neural networks and learning systems
|October 5, 2023
概括
一个新的动态增益固定时间 (FXT) 强大的零化神经网络 (DFTRZNN) 模型解决了复杂的时间变量问题. 这种FXT模型为四次数最小平方问题提供了卓越的稳定性和稳定性.
科学领域:
- 神经网络的神经网络的神经网络
- 控制系统 控制系统
- 数字分析 数字分析
背景情况:
- 传统的算法与时间变量问题作斗争.
- 四边形最小平方问题需要专门的解决方案.
- 现有的零神经网络 (ZNN) 模型存在局限性.
研究的目的:
- 提出一个动态增益固定时间 (FXT) 强大的归零神经网络 (DFTRZNN) 模型.
- 为了解决时间变量等式受约束四次最小方程 (TV-EQLS) 问题.
- 为了提高解决复杂动态系统的稳定性和稳定性.
主要方法:
- 开发一种新的DFTRZNN模型,其中包含一个动态增益参数和一个新的激活函数 (NAF).
- 对FXT稳定性和稳健性的全面理论推导和分析.
- 模拟DFTRZNN模型用于解决TV-EQLS.的模拟.
主要成果:
- 该DFTRZNN模型有效地解决了电视-EQLS问题.
- 在时间变量问题上表现出优于传统的数值算法.
- 通过理论分析和模拟验证了FXT的稳定性和稳定性.
结论:
- 拟议的DFTRZNN模型是TV-EQLS.的优越方法.
- 该模型的设计方案在多代理系统共识中具有实际应用.
- DFTRZNN模型在动态系统中提供了增强的性能和适用性.
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