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13M051MSC - Soft-computing Methods

Course specification
Course title Soft-computing Methods
Acronym 13M051MSC
Study programme Electrical Engineering and Computing
Module Applied Mathematics, Audio and Video Communications, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering, System Engineering and Radio Communications
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 or genetic algorithms with the final aim to solve some control, signal processing or pattern recognition problem.
    The outcome Students should have skills to, according to the problem to be solved, make a choice of a proper soft-computing technique, define necessary a prior knowledge, generate or acquire corresponding algorithm training set, make a fine tuning of controlling parameters, design an algorithm and evaluate it.
    Contents
    Contents of lectures Structure of genetic algorithms; Selection, crossing-over, mutation; Optimization of criteria with constrains based on genetic algorithms; 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; Optimization of particular problem using genetic algorithm; Design of associative memory or process controller based on neural network.
    Literature
    1. C. Lin, C. Lee, Neural Fuzzy Systems, Prentice Hall, 1995
    2. L. Fortuna, G. Rizzoto et al., Soft computing, Springer, 2001
    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 60
    Practical lessons 40 Oral examination 0
    Projects
    Colloquia 0
    Seminars 0