具有循环神经网络的储系统的普遍性
Hiroki Yasumoto1, Toshiyuki Tanaka1
1Graduate School of Informatics, Kyoto University, 36-1, Yoshida Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan.
概括
循环神经网络 (RNN) 储存系统在近似动态系统方面表现出一致的强大通用性. 通过调整线性读数,可以在定义的误差范围内精确地近似目标系统.
科学领域:
- 机器学习 机器学习
- 动态系统理论 动态系统理论
- 计算神经科学是一种神经科学.
背景情况:
- 储库计算利用循环神经网络 (RNN) 来建模复杂的系统.
- 了解基于RNN的水库的近似能力对于它们的应用至关重要.
- 动态系统由于其不断变化的,时间依赖的性质而存在挑战.
研究的目的:
- 调查RNN水库系统的近似能力.
- 为了证明一个特定类型的动态系统的统一强大的普遍性.
- 建立一种用于构建具有可预测的近似误差的RNN储库的方法.
主要方法:
- 对RNN水库系统的理论分析.
- 使用并行连接构建一个RNN水库.
- 统一强大的普遍性的数学证明.
主要成果:
- 对于一类动态系统而言,RNN水库系统表现出一致的强大通用性.
- 通过调整线性读数输出,可以实现近似精度.
- 一个具有平行连接结构的RNN储存器提供了一个独立于目标系统的错误限制.
结论:
- RNN储系统是接近复杂动态系统的强大工具.
- 证明的通用性简化了RNN水库的设计和应用.
- 这项工作为在系统识别和控制中使用RNN提供了理论基础.
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