基于集成增强零化神经网络的多层混合变化时间问题解决,用于强大的操纵器控制
Yansong Zhao1, Jingjing Xiong2
1Department of Foreign Languages, Xinyang Normal University, Xinyang 464000, China.
Heliyon
|October 27, 2023
概括
这项研究介绍了一种强大的多任务控制算法,用于使用集成增强零值神经网络的操纵器. 这种新的方法在不确定的环境中提高了控制的稳定性,解决了单任务控制方法的局限性.
科学领域:
- 机器人和控制工程 机器人和控制工程
- 人工智能的人工智能
- 神经网络的神经网络的神经网络
背景情况:
- 操纵器控制面临来自不确定的环境和需要多任务处理的挑战.
- 现有的研究主要针对单任务控制及其稳定性,忽视了多任务稳定性.
- 集成增强的归零神经网络显示出有望稳定地解决时间变化的问题.
研究的目的:
- 为操纵者开发一个强大的多任务控制算法.
- 为了解决多任务操纵器控制的未研究的稳定性.
- 为了提高稳定性,利用集成增强的零化神经网络.
主要方法:
- 用非线性和混合线性方程制定了多任务控制作为一个两层的时间变化的问题.
- 采用集成增强的归零神经网络来解决多层时间变化的问题.
- 开发了一个强大的多任务控制算法,能够抑制各种类型的噪音.
主要成果:
- 拟议的算法在多任务方案中表现出有效性.
- 理论分析证实,与传统算法相比,它具有更高的稳定性.
- 模拟结果验证了开发算法的有效性和稳定性.
结论:
- 集成增强的归零神经网络为强大的多任务操纵器控制提供了有效的解决方案.
- 开发的算法在复杂和不确定的环境中显著提高了稳定性.
- 这项工作通过解决多任务强度的关键方面,推进了操纵器控制领域.
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