A neural network string matcher

Abstract

The aim of this work is to code the string matching problem as an optimization task and carrying out this optimization problem by means of a Hopfield neural network. The proposed method uses TCNN, a Hopfield neural network with decaying self-feedback, to find the best-matching (i.e., the lowest global distance) path between an input and a template. The proposed method is more than 'exact' string matching. For example wild character matches as well as character that never match may be used in either string. As well it can compute edit distance between the two strings. It shows a very good performance in various string matching tasks. © Springer-Verlag Berlin Heidelberg 2007.