Inheritance of Information in ANNs and Equivalence Relations

This paper describes a set of symmetry transformations (STs) that enable a base net to generate the weights of derived nets. The derived nets then map related functions. This process is aligned to equivalence relationships. This allows information reuse and integration to be aligned to specific equivalence relationship axioms. The paper focuses on early (initial) results. The approach introduces two mathematical techniques: symmetry transformations and a distance function, it also contributes to the connectionist domain by aligning weight transformations to equivalence relations, it develops an alternative way of reusing information which used symmetry transforms for generating a hierarchy of neural networks.