The Forgetting Gradient Algorithm for Parameter and Intersample Estimation of Dual-Rate Systems

  • Yang Hui-zhong
  • Tian Jun
  • Ding Feng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


Multirate systems are abundant in process industry, many soft-sensor design problems are related to modeling, parameter identification, or state estimation involving multirate systems. In this paper, a polynomial transformation technique has been used to derive a dual-rate model with a finite number of parameters; based on this model, the dual-rate forgetting gradient algorithm has been used to estimate the model parameters and intersample outputs based on the dual-rate input-output data directly. Furthermore, convergence properties of the algorithms in the stochastic framework are studied and show that 1) the parameter estimation error consistently converges to zero under the persistent excitation condition; 2) the intersample output estimation error is uniformly bounded. Finally, a simulation example show excellent effectiveness in parameter and output estimation.


Model Predictive Control Distillation Column Output Estimation Stochastic Framework Parameter Estimation Error 
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

  • Yang Hui-zhong
    • 1
  • Tian Jun
    • 1
  • Ding Feng
    • 1
  1. 1.Research Center of Control Science and EngineeringSouthern Yangtze UniversityWuxiP.R. China

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