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对于动态最小方程和多代理系统的耐噪声预定义时间收的ZNN模型
Yiwei Li1, Jiaxin Liu1, Lei Jia2
1College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China.
概括
我们介绍了一个QR分解驱动的耐噪声零化神经网络 (QRDN-ZNN) 模型,用于动态最小平方问题. 这种新型模型增强了数值稳定性和准确性,在杂的环境中性能优于现有的方法.
科学领域:
- 计算神经科学是一种计算神经科学.
- 数字分析 数字分析
- 控制理论 控制理论 控制理论
背景情况:
- 归零神经网络 (ZNN) 在动态矩阵方程中是有效的.
- 它们的性能在数值不稳定性和噪声下下降,特别是在不平等的矩阵维度下.
- 动态最小平方 (DLS) 问题对这些挑战特别敏感.
研究的目的:
- 在杂和不稳定的条件下为DLS问题提出一个强大的ZNN模型.
- 为了提高数值稳定性,精度和收速度.
- 为多代理系统开发一个耐噪声的共识协议.
主要方法:
- 将QR分解集成到ZNN框架 (QRDN-ZNN).
- 引入了一种新的激活功能 (N-Af),以提高性能.
- 理论分析和实验验证.
主要成果:
- 与现有的ZNN模型相比,QRDN-ZNN显示出优越的抗噪性能和准确性.
- 与其他最先进的激活功能相比,N-Af提供了更高的准确性和更快的融合.
- 开发并验证了一种新的耐噪声共识协议.
结论:
- QRDN-ZNN模型有效地解决了DLS问题中的数值不稳定性和噪声问题.
- 拟议的模型在准确性和趋同方面提供了显著的改进.
- 以QRDN-ZNN为灵感的共识协议使得在杂环境中可靠的多代理协调成为可能.
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