Non-Iterative Recovery Information Procedure with Database Inspired in Hopfield Neural Networks

Non-Iterative Recovery Information Procedure with Database Inspired in Hopfield Neural Networks

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This work establishes a simple algorithm to recover an information vector from a predefined database available every time.It is considered that the information analyzed may be incomplete, damaged, or corrupted.This algorithm is inspired by Hopfield Neural Networks (HNN), which allows the recursive reconstruction of an information vector through an energy-minimizing optimal process, but this paper presents a procedure that generates results in a single iteration.Images have adjustment slide been chosen for the information recovery application to build the vector information.

In addition, a filter is added to the algorithm to focus on the most important information when reconstructing data, allowing it to work Vacuum with damaged or incomplete vectors, even without losing the ability to be a non-iterative process.A brief theoretical introduction and a numerical validation for recovery information are shown with an example of a database containing 40 images.