Title:  Soft Computing 

Code:  SFC 

Ac.Year:  2019/2020 

Sem:  Winter 

Curriculums:  

Language of Instruction:  Czech 

Private info:  http://www.fit.vutbr.cz/study/courses/SFC/private/ 

Credits:  5 

Completion:  credit+exam (written) 

Type of instruction:  Hour/sem  Lectures  Seminar Exercises  Laboratory Exercises  Computer Exercises  Other 

Hours:  26  0  0  0  26 

 Exams  Tests  Exercises  Laboratories  Other 

Points:  55  15  0  0  30 



Guarantor:  Zbořil František V., doc. Ing., CSc. (DITS) 

Lecturer:  Zbořil František V., doc. Ing., CSc. (DITS) 
Instructor:  Zbořil František, doc. Ing., Ph.D. (DITS) Zbořil František V., doc. Ing., CSc. (DITS) 

Faculty:  Faculty of Information Technology BUT 

Department:  Department of Intelligent Systems FIT BUT 

Substitute for:  

 Learning objectives: 

  To give students knowledge of softcomputing theories fundamentals, i.e. of fundamentals of nontraditional technologies and approaches to solving hard realworld problems.  Description: 

  Soft computing covers nontraditional technologies or approaches to solving hard realworld 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).  Knowledge and skills required for the course: 

 
 Programming in C++ or Java languages.
 Basic knowledge of differential calculus and probability theory.
 Subject specific learning outcomes and competencies: 

 
 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 natureinspired 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.
 Generic learning outcomes and competencies: 

   Syllabus of lectures: 


 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).
 Syllabus  others, projects and individual work of students: 

 Individual project  solving realworld problem (classification, optimization, association, controlling).  Fundamental literature: 


 Aliev,R.A, Aliev,R.R.: Soft Computing and its Application, World Scientific Publishing Co. Pte. Ltd., 2001, ISBN 9810247001
 Kriesel, D.: A Brief Introduction to Neural Networks, 2005, http://www.dkriesel.com/en/science/neural_networks
 Kruse,
R., Borgelt, Ch., Braune, Ch., Mostaghim, S., Steinbrecher, M.:
Computational Intelligence, Springer, second edition 2016, ISBN
9781447172963
 Mehrotra, K., Mohan, C., K., Ranka, S.: Elements of Artificial Neural Networks, The MIT Press, 1997, ISBN 0262133288
 Munakata, T.: Fundamentals of the New Artificial Intelligence, SpringerVerlag New York, Inc., 2008, ISBN 9781846288388
 Rutkowski, L.: Flexible NeuroFuzzy Systems, Kluwer Academic Publishers, 2004, ISBN 1402080425
 Russel,S., Norvig,P.: Artificial Intelligence, PrenticeHall, Inc., 1995, ISBN 0133601242, third edition 2010, ISBN 0136042597
 Study literature: 


 Kriesel, D.: A Brief Introduction to Neural Networks, 2005, http://www.dkriesel.com/en/science/neural_networks
 Munakata, T.: Fundamentals of the New Artificial Intelligence, SpringerVerlag New York, Inc., 2008. ISBN 9781846288388
 Russel, S., Norvig, P.: Artificial Intelligence, PrenticeHall, Inc., 1995, ISBN 0133601242, second edition 2003, ISBN 0130803022, third edition 2010, ISBN 0136042597
 Progress assessment: 

 
 Midterm 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).
 Exam prerequisites: 

  At least 20 points earned during semester (midterm test and project).  
