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Correction of Non-linear Dynamic Properties of Temperature Sensors by the Use of ANN

  • Lidia Jackowska-Strumiłło
Part of the Advances in Soft Computing book series (AINSC, volume 19)

Abstract

The paper presents a new method for correction of dynamic errors by means of Artificial Neural Networks (ANNs), in which an inverse dynamic model of the sensor is realised by a neural corrector. Feedforward multilayer ANNs and a moving window method were applied. The proposed correction technique has been evaluated experimentally for small platinum RTD immersed in oil. The obtained results were compared to classical, linear correction method. Different multilayer perceptron networks were applied as neural correctors. The ANN approach significantly improved correction performance for the sensor exhibiting non-linear behaviour.

Keywords

Artificial Neural Network Step Response Dynamic Error Classical Correction Artificial Neural Network Approach 
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.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Lidia Jackowska-Strumiłło
    • 1
  1. 1.Computer Engineering DepartmentTechnical University of LodzLodzPoland

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