One problem that the backpropagation technique has is that the magnitude of
the partial derivative may be calculated too large or too small. The
Manhattan update algorithm attempts to solve this by using the partial
derivative to only indicate the sign of the update to the weight matrix. The
actual amount added or subtracted from the weight matrix is obtained from a
simple constant. This constant must be adjusted based on the type of neural
network being trained. In general, start with a higher constant and decrease
it as needed.
The Manhattan update algorithm can be thought of as a simplified version of
the resilient algorithm. The resilient algorithm uses more complex techniques
to determine the update value.
Fields inherited from class org.encog.neural.networks.training.propagation.Propagation