Open AccessApplication of the neural network computing technology for calculating the interval-index characteristics of a minimally redundant modular code
Author(s) -
A. F. Chernyavsky,
А. А. Kolyada,
S. Yu. Protasenya
Publication year - 2019
Publication title -
doklady nacionalʹnoj akademii nauk belarusi
Language(s) - English
Resource type - Journals
eISSN - 2524-2431
pISSN - 1561-8323
DOI - 10.29235/1561-8323-2018-62-6-652-660
Subject(s) - modular design , artificial neural network , computer science , coding (social sciences) , normalization (sociology) , algorithm , interval (graph theory) , scalability , code (set theory) , modular neural network , reduction (mathematics) , parallel computing , set (abstract data type) , mathematics , artificial intelligence , time delay neural network , combinatorics , statistics , operating system , geometry , database , sociology , anthropology , programming language
The article is devoted to the problem of creation of high-speed neural networks (NN) for calculation of interval-index characteristics of a minimally redundant modular code. The functional base of the proposed solution is an advanced class of neural networks of a final ring. These neural networks perform position-modular code transformations of scalable numbers using a modified reduction technology. A developed neural network has a uniform parallel structure, easy to implement and requires the time expenditures of the order (3[log 2 b]+ [log 2 k]+6t sum close to the lower theoretical estimate. Here b and k is the average bit capacity and the number of modules respectively; t sum is the duration of the two-place operation of adding integers. The refusal from a normalization of the numbers of the modular code leads to a reduction of the required set of NN of the finite ring on the (k – 1) component. At the same time, the abnormal configuration of minimally redundant modular coding requires an average k-fold increase in the interval index module (relative to the rest of the bases of the modular number system). It leads to an adequate increase in hardware expenses on this module. Besides, the transition from normalized to unregulated coding reduces the level of homogeneity of the structure of the NN for calculating intervalindex characteristics. The possibility of reducing the structural complexity of the proposed NN by using abnormal intervalindex characteristics is investigated.