Reducing the requirements for the corrective ability of classical codes with error detection and correction using preliminary neural network enrichment of biometric data

Abstract

Background. Obtaining numerical estimates of the corrective ability of classical codes with high redundancy and neural network corrective structures by the example of controlling 416 biometric parameters of the handwritten password word “Penza”. Materials and methods. It is proposed to use the error corrector setting for a single code state consisting only of “0” states. Automatic training of the neural network corrector is carried out using the standard algorithm State Standart R 52633.5-2011. Results. By the example of real data, it is shown that the corrective ability of neural network structures made it possible to reduce the flow of errors by half when using a data-enriching network of 416 neurons with four inputs. When using neurons with 8 inputs, it is possible to additionally reduce the number of errors by a further two times. Conclusions. Preliminary neural network enrichment of data before their folding with redundant self-correcting code greatly reduces the requirements for the corrective ability of the code.

Keywords

• artificial neurons
• harmonic mean neurons
• geometric mean neurons
• protection of artificial intelligence