论文标题
使用等级的QR算法进行储层计算计算的时间班次选择
Time-shift selection for reservoir computing using a rank-revealing QR algorithm
论文作者
论文摘要
储层计算是一种复发性的神经网络范式，其中仅训练输出层，在诸如非线性系统的预测和控制之类的任务上表现出了显着的性能。最近，已经证明，在储层生成的信号中添加时班可以提供大量的性能准确性。在这项工作中，我们提出了一种技术，可以通过使用Rank-Revealing QR算法最大化储层矩阵的等级来选择时间班。这项技术不依赖于任务，不需要系统模型，因此直接适用于模拟硬件储层计算机。我们在两种类型的储层计算机上演示了我们的时班选择技术：一台基于光电振荡器的基于$ tanh $激活功能的传统重复网络。我们发现，在所有情况下，我们的技术比随机减时选择的精度提高了。
Reservoir computing, a recurrent neural network paradigm in which only the output layer is trained, has demonstrated remarkable performance on tasks such as prediction and control of nonlinear systems. Recently, it was demonstrated that adding time-shifts to the signals generated by a reservoir can provide large improvements in performance accuracy. In this work, we present a technique to choose the time-shifts by maximizing the rank of the reservoir matrix using a rank-revealing QR algorithm. This technique, which is not task dependent, does not require a model of the system, and therefore is directly applicable to analog hardware reservoir computers. We demonstrate our time-shift selection technique on two types of reservoir computer: one based on an opto-electronic oscillator and the traditional recurrent network with a $tanh$ activation function. We find that our technique provides improved accuracy over random time-shift selection in essentially all cases.
