advanced data processing GmbH

signal analysis and process modelling

  • correlation, ranked correlation, cluster analysis and feature extraction
  • time series analysis and noise modelling
  • linear process modelling using least square techniques (LS, PCR, PLS, TLS), structure selection methods (forward, backward, stepwise) and validation techniques (cross validation, PRESS, Mallows C(p), adjusted R²)
  • adaptive identification and parameter estimation
  • nonlinear dynamical modelling using filter chains, nonlinear expansion techniques
  • identification of parameter non linear models using optimisation techniques

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