论文标题
通过特征值分析深入了解基于延迟的储层计算
Insight into Delay Based Reservoir Computing via Eigenvalue Analysis
论文作者
论文摘要
在本文中，我们通过特征值分析深入了解基于延迟的储层计算的计算能力。我们集中于任务无关的内存能力，以量化储层性能并将其与动态系统的特征值进行比较。我们表明，这两个量是深入的，因此可以通过分析储层的小信号响应来预测储层计算性能。我们的结果表明，可以通过这种方式分析用作储层的任何动态系统。我们将方法示例应用于具有反馈的光子激光器系统，并将数值计算的召回功能与特征值频谱进行比较。对于系统，特征值的最佳性能是最接近的，其真实零件接近零且非谐振零件。
In this paper we give a profound insight into the computation capability of delay-based reservoir computing via an eigenvalue analysis. We concentrate on the task-independent memory capacity to quantify the reservoir performance and compare these with the eigenvalue spectrum of the dynamical system. We show that these two quantities are deeply connected, and thus the reservoir computing performance is predictable by analyzing the small signal response of the reservoir. Our results suggest that any dynamical system used as a reservoir can be analyzed in this way. We apply our method exemplarily to a photonic laser system with feedback and compare the numerically computed recall capabilities with the eigenvalue spectrum. Optimal performance is found for a system with the eigenvalues having real parts close to zero and off-resonant imaginary parts.
