13M051MSC - Soft-computing Methods

Course specification
Course title Soft-computing Methods
Acronym 13M051MSC
Study programme Electrical Engineering and Computing
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
    ESPB 6.0 Status elective
    Condition none
    The goal Objective of the course is for the students to become able to design neural networks, fuzzy systems and genetic algorithms with the final aim to implement them in process control, signal processing and pattern recognition problems.
    The outcome Students will obtain the skills to choose the adequate soft-computing technique, define necessary a priori knowledge, generate or acquire corresponding algorithm training set, make a fine tuning of controlling parameters, design an algorithm and evaluate it, depending on the problem at hand.
    URL to the subject page
    URL to lectures
    Contents of lectures Structure of genetic algorithms; Selection, crossing-over, mutation; Optimization of criteria with constrains based on genetic algorithms; Travelling salesman problem; Fuzzy sets; Fuzzy operation; Fuzzy algorithms; Design of fuzzy expert systems; Introduction to neural networks; Types of neural networks; Backward error propagation; Application of neural networks; Associative memory.
    Contents of exercises Design of particular fuzzy expert system, design of fuzzy controllers and application of fuzzy algorithm in pattern recognition; Optimization of particular problem using genetic algorithm; Design of process controller or classification system using on neural networks.
    1. L. Fortuna, G. Rizzoto et al., Soft computing, Springer, 2001 (Original title)
    2. C. Lin, C. Lee, Neural Fuzzy Systems, Prentice Hall, 1995 (Original title)
    3. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Pearson Education, 2002 (Original title)
    4. J. R. Jang, C. T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice Hall of India, 2009 (Original title)
    Number of hours per week during the semester/trimester/year
    Lectures Exercises OTC Study and Research Other classes
    3 1
    Methods of teaching Lectures and practical lab work
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures 0 Test paper 70
    Practical lessons 30 Oral examination 0
    Colloquia 0
    Seminars 0