Fuzzy, Evolutionary and Neuro-computing
Students will acquire theoretical foundations, they will recognise possible sutability of the presented methods for the particular problem and will obtain skills in appling fuzzy, evolutionary and neurocomputing methods in variety of scientific, control, financial problems.
- describe and define basic areas of soft computing
- apply fuzzy logic models on control problems
- apply artificial neural networks for prediction and classification tasks
- solve optimization problems using evolutionary computation
- combine different soft computing techniques into complete system
- select the appropriate soft computing method for solving various problems
Forms of Teaching
There are 15 weeks for lectures (2 hours each). Two of these 15 weeks are used for exams. Lectures are divided in two cycles. After each cycle there is an exam.Exams
There is one midterm exam and one final exam.Seminars
Student will have to finish one practical assignment which will be demonstrated to lecturer.
|Type||Threshold||Percent of Grade||Threshold||Percent of Grade|
|Seminar/Project||0 %||20 %||0 %||0 %|
|Mid Term Exam: Written||0 %||40 %||0 %|
|Final Exam: Written||0 %||40 %|
|Exam: Written||50 %||50 %|
|Exam: Oral||50 %|
Week by Week Schedule
- Introduction to fuzzy, evolutionary and neuro-computation. History of the field. Biologicaly inspired computation. Applications and research topics.
- Fuzzy sets, standard notation, properties, examples. Standard min-max operations, S and T norms. Fuzzy relations.
- Fuzzy relations composition, fuzzy graphs. Linguistic variables, linguistic hedges, fuzzy if-then rules.
- Extension principle. Interval and fuzzy aritmetics. Metrics.
- Fuzzy logic, fuzzy rules, generalised modus ponens, extension of fuzzy logic - fuzzy reasoning.
- Fuzzy control systems, fuzzy system structure, types of rules and reasoning, fuzzification and defuzzification methods. case studies.
- Introduction to neural networks. Types of networks and learning methods. Feedforward neural network and backpropagation algorithm.
- Midterm exam
- Self-organizing neural networks and training algorithms.
- Combining neural network and fuzzy logic: neuro-fuzzy systems.
- Genetic algorithms and evolution programs. Principle of evolution. Genetic code. Representation. Initial population. Evaluation function. Genetic operators: selection, crossover and mutation. Genetic algorithm parameters.
- Simple GA. Schema theorem and building block hypothesis. Genetic algorithm in practise. Numerical optimisation.
- Neural network parameters optimisation. Floating-point representation. GA parameters adjusting. Genetic algorithms advantages and disadvantages.
- Using GA for fuzzy model identification. Course summary.
- Final exam.