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Course Information
Course Unit Title : Adaptive Neuro-Fuzzy Inference System (ANFIS)
Course Unit Code : 01INS6130
Type of Course Unit : Optional
Level of Course Unit : Second Cycle
Year of Study : Preb
Semester : 255.Semester
Number of ECTS Credits Allocated : 6,00
Name of Lecturer(s) : ---
Course Assistants :
Learning Outcomes of The Course Unit : Have basic knowledge about artificial intelligence
In general, the rule of recognition and gain information about artificial intelligence methods
Have basic information about the Fuzzy Logic
Ability of ANFIS method and modeling
By doing a modeling example to compare with real data
Mode of Delivery : Face-To-Face
Prerequisities and Co-requisities Courses : Unavailable
Recommended Optional Programme Components : Unavailable
Course Contents : Introduction, fuzzy sets, classical sets, fuzzy sets of operations, fuzzy arithmetic, fuzzy relations, fuzzy relation equations, probability theory, fuzzy logic, uncertainty-based information. Fuzzification / clarification, the rule base, the basic interpretation algorithms, ANFIS modeling.
Languages of Instruction : Turkish
Course Goals : To have basic knowledge about artificial intelligence
In general, the rule of recognition and to gain information about artificial intelligence methods
To have basic information about the Fuzzy Logic
To ability of ANFIS method and modeling
To doing a modeling example to compare with real data
Course Aims : Modeling of engineering problems by using the ANFIS method
WorkPlacement   NA
Recommended or Required Reading
Textbook :
Additional Resources :
Material Sharing
Documents :
Assignments :
Exams :
Additional Material :
Planned Learning Activities and Teaching Methods
Lectures, Practical Courses, Presentation, Seminar, Project, Laboratory Applications (if necessary)
ECTS / Table Of Workload (Number of ECTS credits allocated)
Student workload surveys utilized to determine ECTS credits.
Activity :
Number Duration Total  
Course Duration (Excluding Exam Week) :
14 3 42  
Time Of Studying Out Of Class :
14 6 84  
Homeworks :
2 10 20  
Presentation :
2 2 4  
Project :
0 0 0  
Lab Study :
0 0 0  
Field Study :
0 0 0  
Visas :
1 8 8  
Finals :
1 8 8  
Workload Hour (30) :
30  
Total Work Charge / Hour :
166  
Course's ECTS Credit :
6      
Assessment Methods and Criteria
Studies During Halfterm :
Number Co-Effient
Visa :
1 50
Quiz :
0 0
Homework :
2 50
Attendance :
0 0
Application :
0 0
Lab :
0 0
Project :
0 0
Workshop :
0 0
Seminary :
0 0
Field study :
0 0
   
TOTAL :
100
The ratio of the term to success :
40
The ratio of final to success :
60
TOTAL :
100
Weekly Detailed Course Content
Week Topics  
1 Artificial intelligence basics
  Study Materials: Getting the course syllabus
2 In general, the rule of recognition and artificial intelligence methods
  Study Materials: Research and reading
3 Introduction to Fuzzy Logic method
  Study Materials: Research and reading
4 Sets theory
  Study Materials: Research and reading
5 Aristotelian logic and real-world incompatibilities
  Study Materials: Research and reading
6 Identification of membership functions and accounting methods
  Study Materials: Research and reading
7 Writing rules and its logic
  Study Materials: Research and reading
8 Clarification and its method
  Study Materials: Research and reading
9 Creating a sample model
  Study Materials: Research and reading
10 Creating a sample model
  Study Materials: Research and reading
11 Comparison of model results with real data
  Study Materials: Research and reading
12 Training in Fuzzy Logic
  Study Materials: Research and reading
13 Hybrid modeling and ANFIS method
  Study Materials: Research and reading
14 General Assessment
  Study Materials: Research and reading
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