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network developed in the "learning" process represents a pattern detected in the data.
Thus, in principle, ANN methods can be applied to many research issues such as
those in coastal engineering and oceanography. Theoretically, as long as the training
data set covers the maximum range of the forecasting boundary data, a short-term
data set can be used to train an ANN model for long-term predictions. A trained
neural network can provide a much faster simulation for forecasting long-term events
than traditional hydrodynamic models since its calculation requires no computational
iteration. The implementation of an ANN model is similar to calculating a multiple
variable linear regression function: Output Y(t) = ANN [w1∗X1(t), w2∗ X2(t)
...wn∗Xn(t)], where wi (i = 1, ..., n) are the weights of the ANN network, Xi (i =
1, ..., n) are input signals, and Y is output signal.
3.5. ANN optimization and improvement
The standard gradient-descent training method sometimes suffers from slow con-
vergence due to the presence of one or more local minima. This is generally a charac-
teristic of the particular error surface, which is often composed of several flat and
steep regions. There are, however, several optimization methods, that can be used
to improve the convergence speed and the performance of network training. Details