Shadowing property in analysis of neural networks dynamics

We show that the shadowing and the inverse shadowing, the notions considered in the theory of dynamical systems, may be successfully applied in the analysis of a multilayer neural networks learning process. Our main result is, that generically any such a process is robust. Implementation implications are discussed as well.