450-2029/02 – Artificial Intelligence Systems (SsUI)
Gurantor department | Department of Cybernetics and Biomedical Engineering | Credits | 4 |
Subject guarantor | doc. Ing. Michal Prauzek, Ph.D. | Subject version guarantor | doc. Ing. Michal Prauzek, Ph.D. |
Study level | undergraduate or graduate | Requirement | Optional |
Year | 3 | Semester | winter |
| | Study language | English |
Year of introduction | 2015/2016 | Year of cancellation | |
Intended for the faculties | FEI | Intended for study types | Bachelor |
Subject aims expressed by acquired skills and competences
The subject represents the introduction to the principles of scientific field of artificial intelligence. The goal of subject is introduce students on analysis and design of artificial intelligence tolls in the field of engineering.
Students will be ready for practical use of basic artificial intelligence tools namely fuzzy expert systems, artificial neural networks and genetic algorithms in their diploma works and next praxix.
Teaching methods
Lectures
Individual consultations
Tutorials
Experimental work in labs
Teaching by an expert (lecture or tutorial)
Summary
The course is focused on the area of artificial intelligent methods application in engineering systems. It presents the sources and principles of artificial intelligence, introduces the classical and multivalue logics. The subject introduces the principles of spars knowledge using, principles of fuzzy mathematics, fuzzy logic and its application in modern expert systems and fuzzy controllers. It presents the principles of probability systems, neural networks and genetic algorithms and applications of this tools in modern cybernetics and robotics systems.
Compulsory literature:
Recommended literature:
C. R. REEVES, J. E. ROW,. Genetic Algorithms: Principles and Perspectives. Kluwer Academic Publishers, New York, 2002.
GRAUPE,D. Principles of Artificial Neural Netvorks. World Scientific. 2013. ISBN: 978-981-4522-73-1
Way of continuous check of knowledge in the course of semester
Verification of study:
The current activity of student is done through his laboratory activities.
Conditions for graded credit:
Student can gain up to 40 (min 21) points from the laboratory works and 60 (min 30) points from the final written proof.
E-learning
Other requirements
There are not defined other requirements for student
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Lectures:
1. Introduction on artificial intelligence scientific field. Principles of artificial intelligence, importace of knowledge in problem solving tasks
2. Problem solving procedures in classical logic
3. Methods of computer knowledge reprezentation
4. Mathematic and linguistc modelling
5. Vagueness formalization of knowledge in linguistic models
6. Principles of fuzzy set and fuzy logic theory
7. Fuzzy models of Mandami type, fuzzy controllers Mandami type
8. Fuzzy models of Takagi-Sugeno type, T-S controlles
9. Diagnostic and planning expert systems
10. Probabilistic expert systems
11. Topology and functions of multilayer artificial neural networks
12. Genetic algorithms - versatil optimization methods
13. Neural network optimization using GA
14. Fuzzy controller optimization using GA
Laboratories:
1. Fuzzy controller using microcomputer
2. Fuzzy controller using PLC
Computer labs:
1. Computer system MATLAB, Fuzzy ToolBox
2. Fuzzy sets and vagueness objects reprezentation in MATLAB
3. Fuzzy conditonal rules formalization in MATLAB, fuzzy modelling of Mandami type
4. Fuzzy modelling of Takagi-Sugeno type in MATLAB
5. Fuzzy controllers of Mandami and T-S types
6. Diagnostic fuzzy expert systems
7. Fuzzy expert systems in practice
8. Probalilistic shell expert systém FEL-EXPERT
9. Neural network synthesis in Neural ToolBoxu of MATLAB
10. Neural controller
11. Real genetic algorithm synthesis in MATLAB
12. Fuzzy controller optimization using genetic algorithm
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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