Course detail

Soft Computing

FIT-SFCAcad. year: 2022/2023

Soft computing covers non-traditional technologies or approaches to solving hard real-world problems. Content of course, in accordance with meaning of its name, is as follow: Tolerance of imprecision and uncertainty as the main attributes of soft computing theories. Neural networks. Fuzzy logic. Nature inspired optimization algorithms. Probabilistic reasoning. Rough sets. Chaos. Hybrid approaches (combinations of neural networks, fuzzy logic and genetic algorithms).

Language of instruction

Czech

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

doc. Ing. Vladimír Janoušek, Ph.D.

Department

Department of Intelligent Systems (UITS)

Learning outcomes of the course unit

Students will acquaint with basic types of neural networks and with their applications.
Students will acquaint with fundamentals of theory of fuzzy sets and fuzzy logic including design of fuzzy controller.
Students will acquaint with nature-inspired optimization algorithms.
Students will acquaint with fundamentals of probability reasoning theory.
Students will acquaint with fundamentals of rouhg sets theory and with use of these sets for data mining.
Students will acquaint with fundamentals of chaos theory.

Students will learn terminology in Soft-computing field both in Czech and in English languages.
Students awake the importance of tolerance of imprecision and uncertainty for design of robust and low-cost intelligent machines.

Prerequisites

Programming in C++ or Java languages.
Basic knowledge of differential calculus and probability theory.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Mid-term written examination - 15 points.
Project - 30 points.
Final written examination - 55 points; The minimal number of points necessary for successful clasification is 25 (otherwise, no points will be assigned).

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

To give students knowledge of soft-computing theories fundamentals, i.e. of fundamentals of non-traditional technologies and approaches to solving hard real-world problems.

Specification of controlled education, way of implementation and compensation for absences

Not applicable.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Kriesel, D.: A Brief Introduction to Neural Networks, 2005, Fundamentals of the New Artificial Intelligence, Springer-Verlag New York, Inc., 2008. ISBN 978-1-84628-838-8
Rutkowski, L.: Flexible Neuro-Fuzzy Systems, Kluwer Academic Publishers, 2004, ISBN 1-4020-8042-5
Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., third edition 2010, ISBN 0-13-604259-7

Type of course unit

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Vladimír Janoušek, Ph.D.

Syllabus

Introduction. Biological and artificial neuron, artificial neural networks.
Acyclic and feedforward neural networks, backpropagation algorithm.
Neural networks with RBF neurons. Competitive networks.
Neocognitron and convolutional neural networks.
Recurrent neural networks (Hopfield networks, Boltzmann machine).
Recurrent neural networks (LSTM, GRU).
Genetic algorithms.
Optimization algorithms inspired by nature.
Fuzzy sets and fuzzy logic.
Probabilistic reasoning, Bayesian networks.
Rough sets.
Chaos.
Hybrid approaches (neural networks, fuzzy logic, genetic algorithms).

Project

26 hod., compulsory

Teacher / Lecturer

doc. Ing. Vladimír Janoušek, Ph.D.

Syllabus

Individual project - solving real-world problem (classification, optimization, association, controlling).

Elearning

eLearning: currently opened course

VUT

Faculties

University Institutes

Parts

Soft Computing

Type of course unit