Generating weights and generating vectors to map complex functions with artificial neural networks

The generation of weights is an alternative method of loading a set of weights into an artificial neural network. It is a process that transforms a trained base net by multiplying its weights by symmetric matrices [1]. These weights are then assigned to a derived net. The derived nets map symmetrically related functions. At present, the process is limited because it cannot be applied to one-to-many functions. In this paper, this limitation is overcome by generating a set of vectors from the transformed derived nets that are then used to train an ANN to map one-to-many tasks. The associated rotational symmetries performed are also specified.