提出评估无限长数据下残差计算性能的新方法
Asymptotic evaluation of the information processing capacity in reservoir computing
- 基于渐近展开与加权最小二乘法估算无限长数据的性能
- 数值仿真验证了该方法在评估性能时的有效性
- 适合关注残差计算系统长期性能评估的研究者
残差计算（RC）因训练时间短而日益重要。以目标输出归一化的平方误差称为信息处理能力（IPC），用于评估RC系统的性能。由于RC旨在学习输入与输出时间序列之间的关系，应评估无限长数据下的IPC，而非有限长度数据。然而，尚无有效估计方法。本文通过IPC的渐近展开与加权最小二乘拟合，评估了无限长数据下的IPC，并通过数值模拟验证了方法的有效性。该工作使RC性能评估更加明确。
原文摘要 · Abstract (English)
Reservoir computing (RC) is becoming increasingly important because of its short training time. The squared error normalized by the target output is called the information processing capacity (IPC) and is used to evaluate the performance of an RC system. Since RC aims to learn the relationship between input and output time series, we should evaluate the IPC for infinitely long data rather than the IPC for finite-length data. However, a method for estimating it has not been established. We evaluated the IPC for infinitely long data using the asymptotic expansion of the IPC and weighted least-squares fitting. Then, we showed the validity of our method by numerical simulations. This work makes the performance evaluation of RC more evident.
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