Direct Inverse Control of Sensors by Neural Networks for Static/Low Frequency Applications

  • N. Steele
  • E. Gaura
  • R. J. Rider


This paper addresses the issue of direct inverse control for two types of nonlinear transducer systems characterised by:
  • piecewise linear input-output transfer function;

  • hysteresis occurring in the input-output transfer function; with the aim of establishing whether some relationship exists between the severity of different nonlinearities and the complexity of the network required to control such nonlinearities in static/low-frequency sensor applications.

The compensation is performed using an artificial neural networks approach. The networks chosen were a static MLP and a tap-delayed line MLP, both trained by an improved BKP method which included a form of dynamic learning management.


Network Configuration Neural Network Architecture Artificial Neural Network Approach Network Approximation Seismic Mass 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kraft, M.: “Closed-loop accelerometer employing oversampling conversion”, PhD Thesis, Coventry University, 1997.Google Scholar
  2. 2.
    Gaura E., Burian A., “A dedicated medium for the synthesis of BKP networks”, Romanian J. of Biophysics, Vol. 5, No. 15, 1995, Bucharest, Romania.Google Scholar
  3. 3.
    Irwin, G.W., Warwick, K., Hunt, K.J., “Neural networks applications in control”, IEE Control Engineering Series 53, Short Run Press Ltd., UK, 1995Google Scholar
  4. 4.
    Godjevac, J., Steele, N. “Fuzzy systems and neural networks”, Autosoft J. Intelligent Automation and Soft Computing, 1995Google Scholar
  5. 5.
    Poopalasindam, S., “Neural network based digital compensation schemes for industrial pressure sensors”, Ph.D. Thesis, Coventry University, Sep 1995Google Scholar
  6. 6.
    Trifa, R. Munteanu, E. Gaura, “Neural Network based modelling and simulation of PM-Hybrid Stepping Motor Drives”, Proc. International Aegean Conference on Electrical Machines and Power Electronics, Vol. 2/2, pp. 460–464, Kusadasi, Turkey.Google Scholar

Copyright information

© Springer-Verlag Wien 1999

Authors and Affiliations

  • N. Steele
    • 1
  • E. Gaura
    • 1
  • R. J. Rider
    • 1
  1. 1.Nonlinear Systems Design GroupCoventry UniversityCoventryUK

Personalised recommendations