W. Huang et al. / Ocean Engineering 30 (2003) 22752295
2282
vector W that provides the best possible approximation of the function g(X) based
on the training input [X].
The standard or basic training method is the Gradient Descent Method. In this
method, weight changes move the weights in the direction where the error declines
most quickly. Training is carried out by assigning random initial weights to each of
the neurons (usually between 0.1 and 1.0) and then presenting sets of known input
and target (output) values to the network. The network estimates the output value
from the inputs, compares the model predicted output to the target value, and then
adjusts the weights in order to reduce the mean squared difference between the net-
work output and the target values. The complete inputoutput sets are often run
through the network for several iterations (or epochs) until either the mean square
error is reduced to a given level or reaches a minimum, or until the network has
been trained for a given number of iterations.
If we let wm represent the value of weight w after mth iteration in a neuron, then
wm
wm
wm
(6)
1
where wm is the change in the weight w at the end of iteration m. It is calculated by
wm
edm
(7)