But that growth also has made having an ESN more critical in terms of locating expertise in a far-flung company, according to Ross.
Another successful ESN grew up in an unlikely place: the U.S.
Rather than building something in-house, Reid's KM team chose to use an ESN developed at an office at the Army's Aberdeen Proving Grounds (apg.army.
A new form of RNN training methods, echo state network (ESN), has been proposed by Jaeger and Haas , which is simple and applicable for time series prediction with high accuracy and computational efficiency.
The basic architecture of ESN with K input units, N neurons in the dynamic reservoir, and L output units is shown in Figure 1.
A trained ESN ([W.sub.in], W, [W.sub.fb], [W.sub.out]) is such that its output y(n) of the input u(n) is close to desired output d(n), where [W.sub.in], W, [W.sub.fb], and [W.sub.out] are input, internal, feedback, and output weight matrices, respectively, with suitable sizes.
where f is a state activation function which is usually a sigmoid function and W is the internal recurrent connection weight matrix, of which the spectral radius should be smaller than unity in order to ensure the ESN to work; in other words, W should satisfy the ESP.
Except the reservoir construction, the input layer in deterministic ESN is fully connected to the reservoir and all the input connections have the same absolute weight value v > 0 in contrast to randomly generated input weights in standard ESN.
In this experiment, each ESN model (standard ESN, DLR, DLRB, and SCR) is built with 96 internal units and spectral radius is set to be 0.8.
Each deterministic ESN model is compared with standard ESN model by average forecasting accuracy.