455-0100/01 – Knowledge Based Control Systems (ZSR)
Gurantor department | Department of Measurement and Control | Credits | 4 |
Subject guarantor | prof. Dr. Ing. Miroslav Pokorný | Subject version guarantor | prof. Dr. Ing. Miroslav Pokorný |
Study level | undergraduate or graduate | Requirement | Choice-compulsory |
Year | | Semester | winter |
| | Study language | Czech |
Year of introduction | 2002/2003 | Year of cancellation | 2002/2003 |
Intended for the faculties | FEI | Intended for study types | Master |
Subject aims expressed by acquired skills and competences
The aim of the course is to acquaint students with fuzzy expert systems and fuzzy controllers including its design metodology
After passing the course students have the main knowledge and skills with knowledge systems and their using. Students are able to develop the fuzzy oriented expert or control system.
After passing the course students have the main knowledge and skills with knowledge systems and their using. Students are able to develop the fuzzy oriented expert or control system.
Teaching methods
Lectures
Individual consultations
Tutorials
Experimental work in labs
Project work
Summary
The aim of the course is to acquaint students with fuzzy expert systems and fuzzy controllers including its design metodology After passing the course students have the main knowledge and skills with knowledge systems and their using. Students are able to develop the fuzzy oriented expert or control system.
The course is focused on the area of modern artificial intelligence methods application in the information and control systems. Introduces the complex systems modelling problems and linguistic describing of its behaviour using the experts knowledge. Basic procedures of fuzzy mathematics and fuzzy logic are presented including the fuzzy expert and fuzzy control systems development methodology. Next the course presents the principles of knowledge intelligent cotrol using the neural networks and genetic algorithms.
Compulsory literature:
Novák,V.: Fuzzy množiny a jejich aplikace, SNTL Praha, 1992.
Pokorný,M.:Umělá inteligence v modelování a řízení, BEN Praha, 1996.
Vysoký,P.: Fuzzy řízení, ČVUT Praha, 1996.
Cox,E.: The Fuzzy Systems Handbook, Academic Press, Inc., 1994.
Firemní materiály MOTOROLA.
Goldberg,D.E.: Genetic Algorithms, Addison-Weslley Publishing, 1989
Novák,V.: Základy fuzzy modelování, BEN Praha, 2000
Recommended literature:
Studijní materiály pro studenty kombinovaného studia jsou k dispozici na adrese http://kat455.vsb.cz
Way of continuous check of knowledge in the course of semester
Verification of study:
90 points for elaboration of 3 written tests
Conditions for credit:
10 points for elaboration of individual semestral project (expert system or fuzzy controller)
E-learning
Other requirements
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Lectures:
Complex system modelling principles using numerical and knowledge approaches.
Fuzzy sets theory
Uncertainty and its fuzzy representation
Multivalues fuzzy linguistic logic
Fuzzy linguistic models, Mamdani and Takagi-Sugeno fuzzy rules oriented models
Aproximative reasoning, inference algorithms
Fuzzy expert systems, design methodology, application
Rule base cognitive analysis
Fuzzy control, design methodology, application
Fuzzy controller properties and stability
Intelligent knowledge controllers, architecture and properties
Neural networks in control systems
Genetic algorithms in control systems
Laboratories:
Introduction to laboratory praxis
Motion system analysis of mobile robot model VIMR
Orientation system analysis of mobile robot model VIMR
Motion trajectory definition and realisation in VIMR control system
Communication in control system VIMR
On board microprocessor VIMR control system
Hierarchical control robot system VIMR using PC and HC12
Projects:
Individual semestral project (expert system or fuzzy controller)
Computer labs:
Fuzzy procedures application using MATLAB
Fuzzy linguistic models design using LMPS and NEFRIT
Zadeh´s interpolation method of approximative reassoning
Design of expert systems using LMPS programme system
Fuzzy control mobile robot VIMR
Fuzzy control development using programme system FREG
Fuzzy microprocessor HC121, control system of mobile robot VIMR
Neural and fuzzy neural networks development using programme systems NEUREX and FUZNET
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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