Update-Based Machine Learning Classification of Hierarchical Symbols in a Slowly Varying Two-Way Relay Channel

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00343749" target="_blank" >RIV/68407700:21230/20:00343749 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/math8112007" target="_blank" >https://doi.org/10.3390/math8112007</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/math8112007" target="_blank" >10.3390/math8112007</a>

Result language

angličtina

Original language name

Update-Based Machine Learning Classification of Hierarchical Symbols in a Slowly Varying Two-Way Relay Channel

Original language description

This paper presents a stochastic inference problem suited to a classification approach in a time-varying observation model with continuous-valued unknown parameterization. The utilization of an artificial neural network (ANN)-based classifier is considered, and the concept of a training process via the backpropagation algorithm is used. The main objective is the minimization of resources required for the training of the classifier in the parametric observation model. To reach this, it is proposed that the weights of the ANN classifier vary continuously with the change of the observation model parameters. This behavior is then used in an update-based backpropagation algorithm. This proposed idea is demonstrated on several procedures, which re-use previously trained weights as prior information when updating the classifier after a channel phase change. This approach successfully saves resources needed for re-training the ANN. The new approach is verified via a simulation on an example communication system with the two-way relay slowly fading channel.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Mathematics

ISSN

2227-7390

e-ISSN

2227-7390

Volume of the periodical

8

Issue of the periodical within the volume

11

Country of publishing house

CH - SWITZERLAND

Number of pages

11

Pages from-to

1-11

UT code for WoS article

000593261100001

EID of the result in the Scopus database

2-s2.0-85096010329