一个预定义时间的自适应式零化神经网络,用于解决时间变量线性方程,并将其应用于UR5机器人
IEEE transactions on neural networks and learning systems
|March 20, 2024
概括
一个新的预定义时间自适应式零化神经网络 (PTAZNN) 模型为时间变量线性方程 (TVLEs) 提供了更快,更强大的解决方案. 这种自适应模型可以提高工程应用中的计算效率和抗噪能力.
科学领域:
- 工程数学 工程数学 工程数学
- 计算神经科学是一种计算神经科学.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 时间变量线性方程 (TVLE) 在工程中至关重要,但在计算时间和噪声方面面临挑战.
- 现有的零神经网络 (ZNN) 模型具有潜力,但需要提高效率和稳定性.
研究的目的:
- 提出一种新的预定义时间自适应式零化神经网络 (PTAZNN) 模型来解决TVLE.
- 与现有的ZNN模型相比,增强融合速度和计算资源利用率.
主要方法:
- 开发一个具有独特基于错误的自适应参数的PTAZNN模型.
- 对模型的稳定性,收性和强度进行严格的数学分析.
- 数字模拟和应用到UR5机器人的反向动力学.
主要成果:
- PTAZNN模型显示了比变量参数ZNN模型更短的收时间.
- 这款车型表现出卓越的坚固性和抗噪力.
- 在CoppeliaSim.Sim中成功应用于UR5机器人的反向动力学问题.
结论:
- PTAZNN模型有效地解决了TVLE现有方法的局限性.
- 适应性参数策略提高了计算效率.
- 该模型显示了现实世界工程应用的实际可行性和潜力,包括机器人.
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