Ilipbaeva L.B., Utebaeva D.Z.
ANALYSIS OF MATHEMATICAL MODELS OF NEURAL NETWORKS IN ENCODING INFORMATION
KEY WORDS: NEURAL NETWORK, MATHEMATICAL MODELS, CODING .
Currently, neural networks represent
a future technology that enables to solve various problems in diverse fields of
human activity.
This technology of calculations,
modeling analysis provides new approaches to the study of difficult tasks in
the telecommunications industry, medicine, economics and it is the actual
direction of modern science.
Application of neural networks in
telecommunication systems are due to their following advantages:
- many parallel processing of
information;
- the ability to get the result in
complex, intractable problems.
Neural networks make it possible to
model and analyze nonlinear processes. Their ability to work with large amounts
of data and make it possible to apply adaptable neural network for solving various
problems in telecommunication systems. In recent years, based on neural
networks have been proposed and developed many models for use in routing
topics, distribution channels in mobile radio systems and recognition of speech
signals and transmitting information.
The aim of the this work is the use
of the classical models of neural network in coding theory, and to study the
neural network model's work. The most pressing problem in the transmission of
information in telecommunication systems is the accuracy of the transmitted and
received data. For this purpose is used a quantity of methods of
error-correcting codes: cyclic codes, convolution codes, etc.
Commonly used codes in the
transmission of information theory are cyclic codes. These codes are popular
because of their ease of engineering implementation of decoders, as well as the
high noise immunity. From the classical sources [1,2] it is known that cyclic
codes can detect five or more errors.
Cyclic codes are class of systematic
block codes. In cyclic codes, each combination is coded independently in the
form of blocks of images of polynomials [Figure 1]. Information and control
characters are always at certain places. The basic formula is the construction
of cyclic codes:
F(X)=U(X)P(X)=G(X)Xm +R(X)
where
which reflects
both the method of constructing cyclic code.
Figure 1 - the block scheme of model
This code is used when the data
packet. Upon receipt of the packet, the received packet is divided by the
generator polynomial. If the result is "0" then the combination
adopted without distortion, in the opposite case, there is the "1",
then the package is erroneous.
In this article we discussed about analysis of mathematical models of neural networks in encoding
information. Much commonly models of neural networks in encoding information are
cyclic codes, convolution codes, etc. The advantage of
codes: each combination is coded independently in the form of blocks of images
of polynomials.
THE REFERNCES:
1. Комашинский В.И.,
Смирнов Д.А. Нейронные сети и их применение в системах управления и связи. М.: Горячая
линия – Телеком, 2003. 94 С., 2002.
2
Хайкин
Саймон нейронные сети : М.:
Санкт-Петербург.:-Киев.: 2006.