Course
CI - Computational Intelligence
PDF Course Catalog Deutsche Version: CI
Version: 1 | Last Change: 25.09.2019 18:14 | Draft: 0 | Status: vom verantwortlichen Dozent freigegeben
| Long name | Computational Intelligence |
|---|---|
| Approving CModule | CI_MaTIN |
| Responsible |
Prof. Dr. Rainer Bartz
Professor Fakultät IME |
| Level | Master |
| Semester in the year | summer semester |
| Duration | Semester |
| Hours in self-study | 78 |
| ECTS | 5 |
| Professors |
Prof. Dr. Rainer Bartz
Professor Fakultät IME |
| Requirements | vector functions, gradient |
| Language | German, English if necessary |
| Separate final exam | Yes |
Literature
Domschke W., Drexl A.; Einführung in Operations Research; Springer
Zell, A.: Simulation Neuronaler Netze; Oldenbourg
Nauck, D. et al.: Neuronale Netze und Fuzzy-Systeme; Vieweg
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing; Springer
Gerdes, I. et al.: Evolutionäre Algorithmen; Vieweg
Grosse et al.: Taschenbuch der praktischen Regelungstechnik, Fachbuchverlag Leipzig
Zell, A.: Simulation Neuronaler Netze; Oldenbourg
Nauck, D. et al.: Neuronale Netze und Fuzzy-Systeme; Vieweg
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing; Springer
Gerdes, I. et al.: Evolutionäre Algorithmen; Vieweg
Grosse et al.: Taschenbuch der praktischen Regelungstechnik, Fachbuchverlag Leipzig
Final exam
Details
written examMinimum standard
roughly 50%Exam Type
written examLearning goals
Knowledge
Optimization strategies
- classification of problems
- gradient algorithms
- simplex algorithm
- multiobjective optimization and Pareto approach
Artificial neural networks
- artificial neurons
- neural network structures
- training algorithms
Fuzzy logic
- fuzzification
- inference
- defuzzification
Evolutionary algorithms
- genome representations
- selection mechanisms
- recombination operators
- mutation operators
- classification of problems
- gradient algorithms
- simplex algorithm
- multiobjective optimization and Pareto approach
Artificial neural networks
- artificial neurons
- neural network structures
- training algorithms
Fuzzy logic
- fuzzification
- inference
- defuzzification
Evolutionary algorithms
- genome representations
- selection mechanisms
- recombination operators
- mutation operators
Skills
The students acquire fundamental knowledge on theory and applications of computational intelligence
The students know about typical classes of optimization tasks and how to map a specific problem to those classes
They know the simplex algorithm and can transform problems into the standard form to find the solutions
The students can classify artificial neural networks and determine their applicability for specific tasks
They can vary the parameters of neural networks and rate their impact on the results
They can classify training algorithms and understand the backpropagation algorithm
They know about the fuzzy logic approach, can apply it to specific problems and justify the resulting system behavior
The students know how evolutionary algorithms work and can distinguish the variants
They can transform a problem specification into a representation appropriate for an evolutionary algorithm
They can rate selection strategies and define suitable algorithms
The students can solve linear problems with the use of the simplex algorithm
They can apply artificial neural networks to solve problems of modeling and classification
They can define fuzzy logic systems to solve imprecise and vague tasks
They can solve difficult problems heuristically using evolutionary algorithms
The students know about typical classes of optimization tasks and how to map a specific problem to those classes
They know the simplex algorithm and can transform problems into the standard form to find the solutions
The students can classify artificial neural networks and determine their applicability for specific tasks
They can vary the parameters of neural networks and rate their impact on the results
They can classify training algorithms and understand the backpropagation algorithm
They know about the fuzzy logic approach, can apply it to specific problems and justify the resulting system behavior
The students know how evolutionary algorithms work and can distinguish the variants
They can transform a problem specification into a representation appropriate for an evolutionary algorithm
They can rate selection strategies and define suitable algorithms
The students can solve linear problems with the use of the simplex algorithm
They can apply artificial neural networks to solve problems of modeling and classification
They can define fuzzy logic systems to solve imprecise and vague tasks
They can solve difficult problems heuristically using evolutionary algorithms
Expenditure classroom teaching
| Type | Attendance (h/Wk.) |
|---|---|
| Lecture | 2 |
| Exercises (whole course) | 1 |
| Exercises (shared course) | 0 |
| Tutorial (voluntary) | 0 |
Special literature
keine/none
Special requirements
none
Accompanying material
Compendium with course contents (in engl. language)
Exercises and solutions (in engl. language)
Exercises and solutions (in engl. language)
Separate exam
none
Learning goals
Knowledge
Application of artificial neural networks to a classification task
Variation and multiobjective optimization of neural network parameters
Fuzzy-based closed loop control of a system with two inputs
Variation and multiobjective optimization of neural network parameters
Fuzzy-based closed loop control of a system with two inputs
Skills
The students are familiar with tools supporting computational intelligence
The students can vary system parameters, perform test series, and evaluate, present and discuss the results
The students are able to understand, present, analyze and discuss scientific publications
The students are able to solve problems in small teams
They can tackle optimization tasks in a structured and systematic way
They can rate the behavior of a system with regard to objectives and study and improve the behavior through parameter variations
They are able to cope with international scientific publications, understanding, presenting and discussing them in their context
The students can vary system parameters, perform test series, and evaluate, present and discuss the results
The students are able to understand, present, analyze and discuss scientific publications
The students are able to solve problems in small teams
They can tackle optimization tasks in a structured and systematic way
They can rate the behavior of a system with regard to objectives and study and improve the behavior through parameter variations
They are able to cope with international scientific publications, understanding, presenting and discussing them in their context
Expenditure classroom teaching
| Type | Attendance (h/Wk.) |
|---|---|
| Practical training | 1 |
| Tutorial (voluntary) | 0 |
Special literature
keine/none
Special requirements
none
Accompanying material
Specification of the lab tasks (in engl. language)
Electronic documentation of the tools to be used
Electronic documentation of the tools to be used
Separate exam
none
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