Approximation capability of reservoir systems whose reservoir is a recurrent
neural network (RNN) is discussed. In our problem setting, a reservoir system
approximates a set of functions just by adjusting its linear readout while the
reservoir is fixed. We will show what we call uniform strong universality of a
family of RNN reservoir systems for a certain class of functions to be
approximated. This means that, for any positive number, we can construct a
sufficiently large RNN reservoir system whose approximation error for each
function in the class of functions to be approximated is bounded from above by
the positive number. Such RNN reservoir systems are constructed via parallel
concatenation of RNN reservoirs.

讨论了具有循环神经网络（RNN）作为储层的储层系统的近似能力。在问题设置中，储层系统通过调整其线性读出量来近似一组函数，而储层保持固定。我们将展示一类 RNN 储层系统的统一强普适性，用于近似某类函数。这意味着，对于任意正数，我们可以构建一个足够大的 RNN 储层系统，其对于待近似函数类中每个函数的近似误差都有一个上界。这种 RNN 储层系统是通过 RNN 储层的并行级联构造的。