动态LMVE的任意预定义时间合RNN及其在UR3机器人臂控制和多代理系统中的应用
IEEE transactions on cybernetics
|March 3, 2025
概括
一个新的任意预定义时间的融合循环神经网络 (APTC-RNN) 提供了更快的融合,没有参数调整. 这种具有非线性分片激活函数 (NPAF) 的新型RNN可以降低复杂工程任务的计算成本.
科学领域:
- * 控制系统工程 * 控制系统工程
- * 计算神经科学 计算神经科学
- * 机器人和自动化技术
背景情况:
- * 循环神经网络 (RNN),特别是零化神经网络 (ZNN),在解决时间变化的线性矩阵向量方程方面表现出色.
- * 现有的具有预定义时间收 (PTC) 的ZNN需要复杂的参数调整来实现收时间,这在实际应用中带来了挑战.
研究的目的:
- * 引入一种新的任意预定义时间收RNN (APTC-RNN),不需要对参数进行调整以设置收时间.
- * 开发一个APTC-RNN,使用一个新的非线性断片式激活功能 (NPAF) 来提高效率和稳定性.
主要方法:
- *设计了一种新型的任意预定义时间收RNN (APTC-RNN),其中包含一个非线性断片式激活函数 (NPAF).
- * 严格的理论分析和数学推导来证明稳定性和任意预定义时间收 (APTC) 能力.
- * 数字模拟比较APTC-RNN性能与现有的最先进的RNN.
主要成果:
- * 拟议的APTC-RNN实现了任意预定义时间收 (APTC),而不需要明确的上限参数调整.
- *与三个基准RNN相比,APTC-RNN显示出更高的融合速度和准确性.
- * NPAF导致计算成本降低和计算时间加快.
结论:
- *新的APTC-RNN与NPAF提供了一个强大的和高效的解决方案,用于解决随机预定义的收时间的时间变化的线性矩阵向量方程.
- * 该模型的实用性通过成功应用到UR3机器人手臂控制和多代理系统来验证.
- * 这一进步为机器人和复杂系统中的实时控制应用提供了重大潜力.
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