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Output Flow Estimation of Pneumatically Controlled Ventricular Assist Device with the help of Artificial Neural Network

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Book cover Computer Recognition Systems

Part of the book series: Advances in Soft Computing ((AINSC,volume 30))

Abstract

The paper presents a novel approach to the problem of reliable estimation of the output flow going out from pneumatically controlled ventricular assist device (VAD). Among many possibilities, the application of artificial neural network (ANN) has been decided leading to the promising results. The basic difficulty however is to perform the suitable sufficiently exact measurement on the pneumatic side of the assisting system, which allows to avoid the application of the e.g. ultrasound measuring transducers on the hydraulic side, and which makes possible an implementation of the automatic control algorithm in the future for the whole measurement process. It is important however to underline that from the physical properties point of view these two mentioned sides i.e. pneumatic and hydraulic are completely different. Therefore, due to several nonlinearities, application of ANN gives an acceptable solution.

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References

  1. Czak M, Komorowski D, Kustosz R (1998) An automatic control of the driving unit for pneumatic cardiac assist system. 25th Congress ESAO, November, Bologna

    Google Scholar 

  2. Komorowski D, Tkacz E, Kustosz R (2002) An Application of the Neural Network for Output Flow Estimation in the Pneumatically Driven Polish Ventricular Assist Device (POLVAD). 29th ESAO Congress European Society for Artificial Organs, Viena, Austria, The International Journal of artificial Organs, vol. 20, no. 10.

    Google Scholar 

  3. Ljung J (1987) System Identification: Theory for User. Prentice Hall, Englewood Cliffs, NY.

    MATH  Google Scholar 

  4. Ljung L, Soderstrom T (1983) Theory and Practice of Recursive Identification. MIT Press, Cambridge, Massachusetts.

    MATH  Google Scholar 

  5. Osowski S (1996) Sieci neuronowe w ujêciu algorytmicznym. WNT Warszawa

    Google Scholar 

  6. Sjoberg J (1993) Regularization issues in neural network models of dynamical systems. Linkoping studies in science and technology. thesis no.386, liu tek lic 1993:08, isbn 91-7871-072-3, issn 0280-7971, Department of Electrical Engineering, Linkoping University, Sweden.

    Google Scholar 

  7. Sjoberg J (1995) Non-Linear System Identification with Neural Networks’. PhD thesis Department of Electrical Engineering, Linkoping University, Sweden

    Google Scholar 

  8. Sontag E (1993) Neural networks for control. In H. Trentelman, and J. Willems, editors, Essays on Control: Perspectives in the Theory and its Applications, volume 14 of Progress in Systems and Control Theory, pages 339–380.

    Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Komorowski, D., Tkacz, E. (2005). Output Flow Estimation of Pneumatically Controlled Ventricular Assist Device with the help of Artificial Neural Network. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_66

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  • DOI: https://doi.org/10.1007/3-540-32390-2_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25054-8

  • Online ISBN: 978-3-540-32390-7

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