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Experimental Modeling (Identification)

  • Building models from data, not from first principles (physical, chemical, ... laws)
  • Improving models from first principles with the help of measurement data (grey-box models)
  • Metamodeling: Fast models than approximate complex, time-consuming calculations. Suited for dynamic simulation, optimization, real-time application
  • Nonlinear and/or dynamic models

Design of Experiments

  • Where to measure?(not location but which operating points?)
  • How to describe the operating regime boundaries?

Input Selection

  • Which inputs are how relevant for modeling?
  • Which inputs have strongly nonlinear effects?

Classification, novelty detection, extrapolation detection, ...

Models: What For?

  • Models are the basis for most advanced techniques in many disciplines.
  • Model-based techniques can be divided into a (sometimes iterative) two-step procedure:
    1. Building a model.
    2. Using a technique based on this model.
  • Applications of models:


 Models: What From?



1. Experimental Modeling (Identification)

  • Local model networks.

2. Design of Experiments (DoE)

  • Optimal maximin latin hypercubes (unsupervised).
  • Optimization of next measurement point to gather most information with HILOMOT-DoE (supervised).

3. Automatic Input Selection

4. Metamodeling

  • For CFD, FEM, and Look-up Tables.

5. Nonlinear Dynamic Models

  • Local model networks with OBF and FIR.

6. Classification

  • Least squares support vector machine.

7. Design of Excitation Signals for Identification

8. Applications

  • Combustion engines
  • Driveability calibration
  • Automatic transmission modeling
  • Structural health monitoring
  • Metamodels for CFD simulations
  • Metamodels for FEM simulations
  • Location estimation for inductive charging