设计,分析和验证耐噪声和无超标的循环神经网络
Lei Jia1, Tiandong Zheng1, Yujie Wu1
1College of Computer Science (College of Software), College of Artificial Intelligence, Inner Mongolia University, Hohhot, 010021, China.
概括
一个新的耐噪和无超标的循环神经网络 (NORNN) 有效地抑制噪声,并消除在时间变化的问题中超标. 这种反复的神经网络显著改善了融合时间,即使在噪音条件下也提供了更好的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 神经网络的神经网络的神经网络
背景情况:
- 具有积分项 (RNN-IT) 的循环神经网络 (RNN) 对于时间变化的问题和噪声排斥是有效的.
- 然而,RNN-IT方法存在超越,在抑制噪音时增加合时间.
研究的目的:
- 提出一种新的耐噪和无超标的循环神经网络 (NORNN).
- 提高RNN的融合速度和噪声处理能力,以应对时间变化的问题.
主要方法:
- 在NORNN架构中引入一个时间变化的额外术语.
- 设计以灵活补偿错误并防止积累,从而抵御噪音并消除超标.
- 理论分析使用两个定理和一个推论来证明收性,耐噪声性和无超标性.
主要成果:
- 拟议的NORNN有效地消除了RNN-IT固有的超越现象.
- 与现有方法相比,在趋同时间方面取得了显著的改善.
- 模拟实验证实了在变时矩阵反转和RPRR操纵器轨迹跟踪方面的卓越性能.
结论:
- 对于受噪声影响的时间变化的问题,NORNN提供了强大的解决方案.
- 与传统的RNN-IT相比,它提供了更快的融合和优越的降噪.
- 在动态系统中,NORNN代表了反复出现的神经网络应用的重大进步.
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