Usage of Regular Features of Partially Defined Functional Neural Networks to Find a Solution

Abstract

In this paper, we propose techniques to improve the efficiency of systems acting with knowledge bases and based on functional neural networks (FN-networks) formalism due to the use of their structural features. The article addresses the case of partially defined FN-networks, which have a regular structure and are set as lists of multiple similar objects and fragments with common structure such that their number is unknown and may be unlimited. It offers an algorithm to find solution, which is based on dynamic formation of limited, fully determined local fragments of partially defined network and subsequent transfer of the results to the entire FN-network, which may be endless.