CAIP 2007: Computer Analysis of Images and Patterns pp 784-791 | Cite as
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.
Keywords
String matching Parallel Chaotic Neural Network TCNN Optimization using Hopfield NNPreview
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