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 optimal time shifts. Our technique maximizes the rank of the reservoir matrix using a rank-revealing QR algorithm and is not task dependent. Further, our technique does not require a model of the system, and therefore is directly applicable to analog hardware reservoir computers. We demonstrate our time-shift optimization 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.

翻译：储层计算是一种仅训练输出层的循环神经网络范式，在非线性系统预测与控制等任务中展现出卓越性能。近期研究表明，对储层生成的信号添加时移可显著提升性能精度。本文提出一种选择最优时移的技术，该技术采用秩揭示QR算法最大化储层矩阵的秩，且不依赖于具体任务。此外，该技术无需系统模型，因此可直接应用于模拟硬件储层计算机。我们在两种储层计算机上验证了所提出的时移优化技术：一种基于光电振荡器，另一种采用传统tanh激活函数的循环网络。实验结果表明，在几乎所有情况下，该技术相比随机时移选择均能提供更优的精度。