This course is intended to provide students with an in depth understanding of the machine learning and artificial intelligence algorithms, as well as advanced issues of intelligent control systems: • artificial intelligence methods based systems identification and control techniques and their applications in development of intelligent control systems; • artificial intelligence methods based classification and recognition techniques and their applications. After the course, the students will be able to apply the learned knowledge to solve problems in their respective research fields.

On successful completion of the course the students should be able to: • Understand of the functional operation of a variety of intelligent control techniques. • Understand the theoretical foundations of intelligent control methods from the point of view of identification and control of dynamic systems. • Carry out synthesis and analysis of intelligent control systems based on combinations of various theories: simulation, neural networks, fuzzy systems, genetic algorithms, evolutionary algorithms, etc.

Introduction to artificial neural networks. Feedforward neural networks. Recurrent neural networks. Radial basis function neural network. Support vector machines. Self-organizing map neural network. Artificial neural networks based identification of nonlinear systems. Artificial neural networks based control of nonlinear systems. Artificial neural networks based image recognition and pattern classification. Genetic algorithm. Evolutionary algorithms. Neuro-fuzzy systems.

PA: Practical work includes computational exercises that follow the content of course. PL: Practice and experiments: computer applications in simulation and implementation of intelligent control systems, as well as their practical realization using Matlab/Simulink and different plants within a modular educational real-time control system (inverted pendulum, double inverted pendulum, DC servo motor, coupled tanks experiment, heat flow experiment).

Defined by curriculum of the study programme.

• Modular educational real time control system with various plants (DC servo motor, inverted pendulum, double inverted pendulum, heat flow experiment, coupled water tanks experiment), with acquisition hardware and software. • Intelligent Control Systems Laboratory.

Total assigned hours: 65

New material: 35
Elaboration and examples (recapitulation): 15

Auditory exercises: 0
Laboratory exercises: 0
Calculation tasks: 0
Seminar paper: 0
Project: 0
Consultations: 0
Discussion/workshop: 0
Research study work: 0

Review and grading of calculation tasks: 0
Review and grading of lab reports: 0
Review and grading of seminar papers: 15
Review and grading of the project: 0
Test: 0
Test: 0
Final exam: 0

Activity during lectures: 0
Test/test: 0
Laboratory practice: 10
Calculation tasks: 0
Seminar paper: 40
Project: 0
Final exam: 50
Requirement for taking the exam (required number of points): 0

Simon Haykin, "Neural Networks and Learning Machines", Vol. 3. Upper Saddle River, NJ, USA:: Pearson, 2009.; Ethem Alpaydin, "Introduction to machine learning", 2nd Edition, The MIT Press, Cambridge, England, 2010.; W. Thomas Miller, Richard S. Sutton, Paul J. Werbos, "Neural networks for control", The MIT Press, Cambridge, MA, USA, 1995.