Intelligent Control Systems
Knowledge about complex neural network controller design. Knowledge about different learning and automated setting methods for parameters tuning of intelligent control algorithms.
- classify artificial neural networks
- apply nonrecursive neural network learning algorithms
- apply recursive neural network learning algorithms
- identify nonlinear dynamical systems via static neural networks
- design a neural controller for control of nonlinear dynamical systems
- apply genetic algorithms in optimization
Forms of Teaching
Lectures are organized in two thematic cycles (neural networks and genetic algorithms).Exams
Seminars. Direct conversation with students in the classroom.Consultations
One hour weekly.Seminars
Neural network design for identification and/or control of nonlinear dynamical system.Acquisition of Skills
Use of Matlab Neural Networks Toolbox.
|Type||Threshold||Percent of Grade||Comment:||Percent of Grade|
|Seminar/Project||0 %||35 %||0 %||35 %|
|Mid Term Exam: Written||0 %||20 %||0 %|
|Final Exam: Written||0 %||20 %|
|Final Exam: Oral||25 %|
|Exam: Written||0 %||40 %|
|Exam: Oral||25 %|
The oral exam share is ±25%. Seminars are obligatory.
Week by Week Schedule
- Introduction to artificial neural networks. Classification of artificial neural networks.
- Static neural networks. MultiLayer Perceptron neural networks. Radial Basis Function (RBF) neural networks.
- Dynamic neural networks. Neural networks training.
- Non-recursive algorithms for neural networks training. Gradient based algorithms.
- Newton algorithms. Quasi-Newton algorithms.
- Recursive learning algorithms.
- Midterm exam
- Neural network (NN) structures suitable for system modeling and control. NN learning algorithms.
- Application of NN in identification and modeling of complex systems. Selection of suitable NN structures and their validation.
- NN process model-based control structures. Application of NN for improvement of nonlinear control system performance.
- Concept of instantaneous NN linearization and its application to linear controller synthesis.
- Application of NN for compensation of system uncertainties.
- Genetic algorithms (GA). Selection of population entities. Crossover operators. Mutation operators. Elitism.
- Selection of penalty functions. Applications of GA.
- Final exam
Control Engineering and Automation -> Electrical Engineering and Information Technology (Profile)
Electrical Engineering Systems and Technologies -> Electrical Engineering and Information Technology (Profile)