Some issues relating to forward and reverse identification of artificial intelligence based systems

Abstract

Systems incorporating neural networks as a component of artificial intelligence have current applications in diverse areas of technology. The feasibility of reverse engineering these systems to gain and discover unknown capabilities is addressed from a generalized point of view. Taking the mathematical inverse function concept as an equivalent of reverse engineering, the problem of identification of nonlinear functions by neural networks is studied. The sinc-function is simulated by partially correlated networks as an illustration. Construction of inverse mapping for a nonlinear map by neural networks is formulated.