UINA345 Expert Systems Applications

Faculty of Philosophy and Science in Opava
Winter 2019
Extent and Intensity
2/2/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Petr Čermák, Ph.D. (lecturer)
doc. Ing. Petr Čermák, Ph.D. (seminar tutor)
RNDr. Jiří Martinů, Ph.D. (seminar tutor)
Guaranteed by
doc. Ing. Petr Čermák, Ph.D.
Institute of Computer Science – Faculty of Philosophy and Science in Opava
Prerequisites (in Czech)
TYP_STUDIA(N)
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The goal of the course is to acquaint students with actual trends in development and implementation of expert systems based on different types of vagueness and also with methods of softcomputing. Practical exercises acquaint students with used empty knowledge-based and/or expert systems with emphasis on integration of fuzzy approaches. Inseparable part of the exercises is also design of knowledge or expert basis of a fuzzy rule based expert system for a priori determined problem in given empty fuzzy rule based knowledge-based or expert system.
Syllabus
  • 1. Subjective knowledge and mental models, language modeling, conditional production rules, set of rules as language model, vagueness of the language model
    2. Vagueness of conditional rules formalized by means of probability level, expert systems with entropy such as MYCIN, EMYCIN and PROSPECTOR
    3. Probability-based rule-based expert systems - FEL-EXPERT
    4. Formalization of vagueness of language terms by means of fuzzy sets, basics of fuzzy mathematics, approximation of language models of fuzzy functions, fuzzification and defuzzification
    5. Fuzzy logic, language variable, interpretation of fuzzy logic functions, fuzzy relations, approximative derivation, fuzzy rule Modus Ponens
    6. Fuzzy approximation of multidimensional non-linear systems, approximative derivation based on Takagi-Sugeno rules
    7. Cognitive analysis of rule base of knowledges, consistency test, diversification of knowledge base, ternary graph
    8. Artificial neural multi-layer networks, adaptation procedures of ANN, fuzzy neural networks, identification of the fuzzy model Takagi-Sugeno
    9. Automatic methods of structural and parametric identification of rule-based fuzzy models, fuzzy clustering methods
    10. Basics of fuzzy regulation, linear regulators in case of feedback regulation, fuzzy regulators synthesis, Sugeno-based regulators
    11. Methods of structural and parametric optimalization of rule-based fuzzy models, optimalization methods based on evolution
    12. Real-time expert system, knowledge management, intelligent regulators, knowledge-based adaptation of structure and parameters of regulators
Literature
    required literature
  • V. Mařík a kol. Umělá inteligence I, II. Praha, 2001. info
  • V. Novák. Základy fuzzy modelování. Praha, 2000. ISBN 80-7300-009-1. info
  • Pokorný, M. Umělá inteligence v modelování a řízení. Praha, 1996. ISBN 80-901984-4-9. info
    recommended literature
  • ZELINKA, I. Evoluční výpočetní techniky ?principy a aplikace. Praha, 2008. ISBN 978-80-7300-218-3. info
  • CZOGALA,E.?LESKI,J. Fuzzy and Neuro-Fuzzy Systems. Berlin, 2000. ISBN 3790812897. info
  • PROVAZNÍK, I.? KOZUMPLÍK. Expertní systémy. Brno, 1999. ISBN 80-214-1486-3. info
  • VYSOKÝ, P. Fuzzy řízení. Praha, 1996. ISBN 80-01-01429-8. info
  • SINČÁK,P.?ANDREJKOVÁ,G. Neuronové sítě 1, 2. Košice, 1996. ISBN 80-88786-42-8. info
Teaching methods
Interactive lecture
Lecture with a video analysis
Assessment methods
Exam
Language of instruction
English
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
Teacher's information
* 75% attendance in seminars, active participation
* written test in the extent of the given literature and the content of seminars 30 points
* implementation of selected methods of softcomputing, success rate 30 points from programming and 10 points from documentation
* 40 points exam
50% all parts
The course is also listed under the following terms Winter 2017, Winter 2018, Winter 2020.
  • Enrolment Statistics (Winter 2019, recent)
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