Image Memory on Pyramidal Neural Networks of Fast Learning

the application of backward-oriented pyramidal neural networks of fast learning for the implementation of image memory elements is considered. Networks of the class under consideration are representable by linear operators, have a self-similar structure, and are a special case of the fast Fourier transform algorithm. Methods of topological construction of pyramidal networks are given. It is proved that pyramid networks belong to the category of networks with a deep degree of learning.