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An intelligent system for tuning magnetic field of a cathode ray tube deflection yoke
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).ORCID iD: 0000-0003-2185-8973
Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
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2003 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 16, no 3, p. 161-164Article in journal (Refereed) Published
Abstract [en]

This short communication concerns identification of the number of magnetic correction shunts and their positions for deflection yoke tuning to correct the misconvergence of colours of a cathode ray tube. The misconvergence of colours is characterised by the distances measured between the traces of red and blue beams. The method proposed consists of two phases, namely, learning and optimisation. In the learning phase, the radial basis function neural network is trained to learn a mapping: correction shunt position→changes in misconvergence. In the optimisation phase, the trained neural network is used to predict changes in misconvergence depending on a correction shunt position. An optimisation procedure based on the predictions returned by the neural net is then executed in order to find the minimal number of correction shunts needed and their positions. During the experimental investigations, 98% of the deflection yokes analysed have been tuned successfully using the technique proposed.

Place, publisher, year, edition, pages
Amsterdam: Elsevier Science , 2003. Vol. 16, no 3, p. 161-164
Keywords [en]
Cathode ray tube, Learning, Neural network, Simulated annealing
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hh:diva-213DOI: 10.1016/S0950-7051(02)00081-3ISI: 000181221200004Scopus ID: 2-s2.0-0037397227Local ID: 2082/508OAI: oai:DiVA.org:hh-213DiVA, id: diva2:237391
Available from: 2006-11-24 Created: 2006-11-24 Last updated: 2018-01-13Bibliographically approved

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Verikas, Antanas

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CiteExportLink to record
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Citation style
  • apa
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