Course details
Soft Computing
SFC Acad. year 2016/2017 Winter semester 5 credits
Guarantor
Language of instruction
Completion
Time span
Assessment points
Department
Lecturer
Instructor
Zbořil František, doc. Ing., Ph.D. (DITS FIT BUT)
Subject specific learning outcomes and competences
 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 learn to solve optimization problems using Genetic, Ant Colony Optimization and Particle Swarm 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 competences

Students will learn terminology in Softcomputing field both in Czech and in English languages.
 Students awake the importance of tolerance of imprecision and uncertainty for design of robust and lowcost intelligent machines.
Learning objectives
Prerequisite kwnowledge and skills
 Programming in C++ or Java languages.
 Basic knowledge of differential calculus and probability theory.
Study literature
 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
 Russel, S., Norvig, P.: Artificial Intelligence, PrenticeHall, Inc., 1995, ISBN 0133601242, second edition 2003, ISBN 0130803022, third edition 2010, ISBN 0136042597
Fundamental literature
 Aliev,R.A, Aliev,R.R.: Soft Computing and its Application, World Scientific Publishing Co. Pte. Ltd., 2001, ISBN 9810247001
 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
Syllabus of lectures
 Introduction. Biological and artificial neuron, artificial neural networks. Basic neuron models, Adaline and Perceptron.
 Madaline and BP (Back Propagation) neural networks. Adaptive feedforward multilayer networks.
 RBF and RCE neural networks. Topologic organized neural networks, competitive learning, Kohonen maps.
 CPN , LVQ and ART neural networks.
 Neural networks as associative memories (Hopfield, BAM, SDM).
 Solving optimization problems using neural networks. Stochastic neural networks, Boltzmann machine.
 Genetic algorithms.
 ACO and PSO optimization algorithms.
 Fuzzy sets, fuzzy logic and fuzzy inference.
 Probabilistic reasoning, Bayesian networks.
 Rough sets.
 Chaos.
 Hybrid approaches (neural networks, fuzzy logic, genetic algorithms).
Syllabus  others, projects and individual work of students
Progress assessment
 Midterm written examination  15 points.
 Project  30 points.
 Final written examination  55 points; The minimal number of points which can be obtained from the final written examination is 25. Otherwise, no points will be assigned to a student.
Exam prerequisites
Course inclusion in study plans
 Programme ITMSC2, field MBI, 2nd year of study, Compulsory
 Programme ITMSC2, field MBS, MGM, MIS, MMI, MSK, any year of study, Elective
 Programme ITMSC2, field MIN, 1st year of study, Compulsory
 Programme ITMSC2, field MMM, any year of study, CompulsoryElective group N
 Programme ITMSC2, field MPV, 2nd year of study, CompulsoryElective group B