IAS0031 Modeling and Identification

The aim of the course is to give an overview of modeling and identification methods for solving static and dynamic problems such as optimal resource planning and industrial control. The major topics covered in the course include:

  • Optimization. Linear programming. Convexity. Least squares. Newton's Method. Nelder-Mead method (applications).
  • Static and dynamic models and applications.
  • Linear models. Time domain and frequency domain analysis.
  • Process models. Mathematical models of industrial processes.
  • Identification. Model types. Validation. Residual analysis.
  • Introduction to nonlinear systems. Linearization.
  • Global optimization methods. Genetic programming and symbolic regression.
  • Computational intelligence. Artificial Neural Networks. Modeling dynamic systems. Pattern recognition.

These topics will be delivered by several instructors and will be accompanied by corresponding practical works.