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  • This article compares two methods of identification of a real system. The first one is the classical Least squares method and the second one is a Neural network approach that uses the feedforward network with tree layers. The Levenberg-Marquardt was chosen as the learning algorithm. Laboratory equipment Amira DR300 was selected for the verification of both methods. It is a servo-mechanism which consists of a permanently excited DC-motor and a tacho-generator with the incremental encoder as a sensor of the output signal.
  • This article compares two methods of identification of a real system. The first one is the classical Least squares method and the second one is a Neural network approach that uses the feedforward network with tree layers. The Levenberg-Marquardt was chosen as the learning algorithm. Laboratory equipment Amira DR300 was selected for the verification of both methods. It is a servo-mechanism which consists of a permanently excited DC-motor and a tacho-generator with the incremental encoder as a sensor of the output signal. (en)
Title
  • A comparative study of a real system identification
  • A comparative study of a real system identification (en)
skos:prefLabel
  • A comparative study of a real system identification
  • A comparative study of a real system identification (en)
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  • RIV/70883521:28140/11:43866798!RIV13-MSM-28140___
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  • 183676
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  • RIV/70883521:28140/11:43866798
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  • Identification, Neural network, Least squares method, Amira DR 300 (en)
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  • [3DA190E41354]
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  • Vienna
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  • Vienna
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  • Annals of DAAAM for 2011 & Proceedings of the 22nd International DAAAM Symposium %22Intelligent Manufacturing & Automation: Power of Knowledge and Creativity%22
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  • Gazdoš, František
  • Pišan, Radim
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issn
  • 1726-9679
number of pages
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  • DAAAM International Vienna
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  • 978-3-901509-83-4
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  • 28140
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