Reservoir computing is a machine learning framework that exploits nonlinear
dynamics, exhibiting significant computational capabilities. One of the
defining characteristics of reservoir computing is its low cost and
straightforward training algorithm, i.e. only the readout, given by a linear
combination of reservoir variables, is trained. Inspired by recent mathematical
studies based on dynamical system theory, in particular generalized
synchronization, we propose a novel reservoir computing framework with
generalized readout, including a nonlinear combination of reservoir variables.
The first crucial advantage of using the generalized readout is its
mathematical basis for improving information processing capabilities. Secondly,
it is still within a linear learning framework, which preserves the original
strength of reservoir computing. In summary, the generalized readout is
naturally derived from mathematical theory and allows the extraction of useful
basis functions from reservoir dynamics without sacrificing simplicity. In a
numerical study, we find that introducing the generalized readout leads to a
significant improvement in accuracy and an unexpected enhancement in robustness
for the short- and long-term prediction of Lorenz chaos, with a particular
focus on how to harness low-dimensional reservoir dynamics. A novel way and its
advantages for physical implementations of reservoir computing with generalized
readout are briefly discussed.

基于广义输出的储层计算框架利用非线性动力学实现重要的信息处理能力，同时保持了储层计算的低成本和线性学习框架的优势，并在预测混沌系统、提取有用基函数等方面取得了显著的精度和鲁棒性的提高。