  We develop new artificial neural network models for graded vector spaces,
which are suitable when different features in the data have different
significance (weights). This is the first time that such models are designed
mathematically and they are expected to perform better than neural networks
over usual vector spaces, which are the special case when the gradings are all
1s.
