| OutputFieldMultiplicative |
* Both the multiplicative and z-axis normalization types allow a group of outputs to be adjusted so that the "vector length" is 1. Both go about it in different ways. Certain types of neural networks require a vector length of 1. The multiplicative normalization is more simple than Z-Axis normalization. Almost always Z=Axis normalization is a better choice. However, multiplicative can perform better than Z-Axis when all of the values are near zero most of the time. This can cause the "synthetic value" that z-axis uses to dominate and skew the answer. Multiplicative normalization works by calculating the vector length of the input fields and dividing each by that value. This also presents a problem, as the magnitude of the original fields is not used. For example, multiplicative normalization would not distinguish between (-2,1,3) and (-10,5,15). Both would result in the same output. |