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Project
Modelling structured dynamical systems: parametric and non-parametric approaches (FWOAL648)
The aim of this project "Modelling structured dynamical systems: parametric and non-parametric approaches" is to realize synergies between different families of modelling and identification techniques for structured nonlinear dynamical systems.
A first class of methodologies relates to support vector machines, originally developed within the machine learning community. A second class is based on classical system identification, which finds its roots mainly within the systems and control community. Parametric and non-parametric approaches will be studied from these different perspectives. This project aims at achieving complementary and unifying insights with:
- a better understanding of the impact of the intrinsic assumptions, e.g. how crucial is it to impose certain models structures in predictive modelling?
- a better understanding of the strong and weak points of the different approaches: why is one approach successful on certain types of problems while it may totally fail on others?
- proposing new methods that combine and integrate the best of different modelling paradigms
- data driven detection of the dynamic structure of the nonlinear system
A first class of methodologies relates to support vector machines, originally developed within the machine learning community. A second class is based on classical system identification, which finds its roots mainly within the systems and control community. Parametric and non-parametric approaches will be studied from these different perspectives. This project aims at achieving complementary and unifying insights with:
- a better understanding of the impact of the intrinsic assumptions, e.g. how crucial is it to impose certain models structures in predictive modelling?
- a better understanding of the strong and weak points of the different approaches: why is one approach successful on certain types of problems while it may totally fail on others?
- proposing new methods that combine and integrate the best of different modelling paradigms
- data driven detection of the dynamic structure of the nonlinear system
Date:1 Jan 2012 → 31 Dec 2015
Keywords:Automatic Measurement Systems, Nonlinear Modelling, Medical Physics, Fibre Optic, Parameter Estimation, Microwaves, Instrumentation, Underwater Acoustics, System Identification, Telecommunications, Electrical Measurements, Nonlinear Measurements, Electromagnetism
Disciplines:Electrical and electronic engineering, Mathematical sciences and statistics, (Bio)medical engineering