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Gradient Approach to Curve Fitting with Incomplete Data

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Challenges in Automation, Robotics and Measurement Techniques (ICA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 440))

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Abstract

The paper deals with the problem of nonlinear curve fitting in situation of incomplete data. Research was motivated by the industrial identification of Hammerstein models. It was noticed that for the model robustness and quality the fitness of the static nonlinear element is much more crucial then efficiency of dynamic operation of linear part. Industrial data are incomplete, i.e. they do not cover the whole process domain. It is due to the operation around selected steady states, close loop operation, extensive manual model use, etc. In case of multi-regional approach we often get regions with no data. Proposed methodology is addressing that issue. Included results are for both simulation and real industrial case.

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References

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Author information

Authors and Affiliations

  1. Institute of Control and Computational Engineering, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warszawa, Poland

    Karol Koniuszewski & Paweł D. Domański

Authors
  1. Karol Koniuszewski
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  2. Paweł D. Domański
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Corresponding author

Correspondence to Karol Koniuszewski .

Editor information

Editors and Affiliations

  1. Automation and Measurements PIAP, Industrial Research Institute for, Warsaw, Poland

    Roman Szewczyk

  2. Automation and Measurements PIAP, Industrial Research Institute for, Warsaw, Poland

    Cezary Zieliński

  3. Automation and Measurements PIAP, Industrial Research Institute for, Warsaw, Poland

    Małgorzata Kaliczyńska

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Koniuszewski, K., Domański, P.D. (2016). Gradient Approach to Curve Fitting with Incomplete Data. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Challenges in Automation, Robotics and Measurement Techniques. ICA 2016. Advances in Intelligent Systems and Computing, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-319-29357-8_25

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  • DOI: https://doi.org/10.1007/978-3-319-29357-8_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29356-1

  • Online ISBN: 978-3-319-29357-8

  • eBook Packages: EngineeringEngineering (R0)

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