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Neural Networks and Cascade Modeling Technique in System Identification

  • Erdem Turker Senalp
  • Ersin Tulunay
  • Yurdanur Tulunay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3949)

Abstract

The use of the Middle East Technical University Neural Network and Cascade Modeling (METU-NN-C) technique in system identification to forecast complex nonlinear processes has been examined. Special cascade models based on Hammerstein system modeling have been developed. The total electron content (TEC) data evaluated from GPS measurements are vital in telecommunications and satellite navigation systems. Using the model, forecast of the TEC data in 10 minute intervals 1 hour ahead, during disturbed conditions have been made. In performance analysis an operation has been performed on a new validation data set by producing the forecast values. Forecast of GPS-TEC values have been achieved with high sensitivity and accuracy before, during and after the disturbed conditions. The performance results of the cascade modeling of the near Earth space process have been discussed in terms of system identification.

Keywords

Total Electron Content Internal Variable Model Predictive Control Cascade Modeling Disturbed Condition 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Erdem Turker Senalp
    • 1
  • Ersin Tulunay
    • 1
    • 2
  • Yurdanur Tulunay
    • 3
  1. 1.Department of Electrical and Electronics EngineeringMiddle East Technical UniversityBalgat, AnkaraTurkey
  2. 2.Information Technologies InstituteTUBITAK Marmara Research CenterGebze, KocaeliTurkey
  3. 3.Department of Aerospace EngineeringMiddle East Technical UniversityBalgat, AnkaraTurkey

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