Study on fading prediction for automated guided vehicle using probabilistic neural network

Julian Webber, Norisato Suga, Abolfazl Mehbodniya, Kazuto Yano, Tomoaki Kumagai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper describes a technique to predict the fading channel of an automated guided vehicle (AGV) that moves along a pre-determined route. A probabilistic neural network (PNN) estimates the most likely signal by performing pattern matching between a stored and current fading signal window. The prediction unit is being developed as part of an anomaly detection unit that together will provide advance information on pending communication outages in a factory communications network. Multiple distributed receivers are employed in order to further improve the accuracy of the prediction. Performance is evaluated using a ray-tracing model of the moving AGV and results show that the mean squared error (MSE) can be reduced four orders of magnitude by employing eight receivers.

Original languageEnglish
Title of host publication2018 Asia-Pacific Microwave Conference, APMC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages887-889
Number of pages3
ISBN (Electronic)9784902339451
DOIs
Publication statusPublished - 2019 Jan 16
Externally publishedYes
Event30th Asia-Pacific Microwave Conference, APMC 2018 - Kyoto, Japan
Duration: 2018 Nov 62018 Nov 9

Publication series

NameAsia-Pacific Microwave Conference Proceedings, APMC
Volume2018-November

Conference

Conference30th Asia-Pacific Microwave Conference, APMC 2018
Country/TerritoryJapan
CityKyoto
Period18/11/618/11/9

Keywords

  • Automated guided vehicle
  • Fading channels
  • Machine-learning
  • Prediction methods
  • Probabilistic neural network

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Study on fading prediction for automated guided vehicle using probabilistic neural network'. Together they form a unique fingerprint.

Cite this