  Reservoir computers (RC) are a form of recurrent neural network (RNN) used
for forecasting time series data. As with all RNNs, selecting the
hyperparameters presents a challenge when training on new inputs. We present a
method based on generalized synchronization (GS) that gives direction in
designing and evaluating the architecture and hyperparameters of a RC. The
'auxiliary method' for detecting GS provides a pre-training test that guides
hyperparameter selection. Furthermore, we provide a metric for a "well trained"
RC using the reproduction of the input system's Lyapunov exponents.
