Publication detail

Application of Neural Networks for Hot-Air System Control

PIVOŇKA, P., VELEBA, V.

Original Title

Application of Neural Networks for Hot-Air System Control

Type

journal article - other

Language

English

Original Abstract

Application of an adaptive semi-inversion neural controller for a laboratory hot-air system model is described. The system can be used to control two parameters – the airflow and the temperature inside the tunnel. The hot-air system displays negative effects commonly occurring in industrial applications – different static amplifications at different operating points, large offset increasing with time, dead zone and noise. The used semi-inversion neural controller is based on an inversion controller, but is capable of solving problems such as oscillating control action, noise sensitivity and ill-estimated parameters in the initial phase of control or adjustment.

Key words in English

Semi-inversion controller, hot-air system, noise rejection, programmable logic controller, PID

Authors

PIVOŇKA, P., VELEBA, V.

RIV year

2004

Released

25. 3. 2004

ISBN

1109-2777

Periodical

WSEAS Transactions on Systems

Year of study

3

Number

2

State

United States of America

Pages from

757

Pages to

760

Pages count

4

BibTex

@article{BUT41840,
  author="Petr {Pivoňka} and Václav {Veleba}",
  title="Application of Neural Networks for Hot-Air System Control",
  journal="WSEAS Transactions on Systems",
  year="2004",
  volume="3",
  number="2",
  pages="4",
  issn="1109-2777"
}